An Account of Global Intra-industry Trade, 1962–2006

The World Economy

(2009)doi: 10.1111/j.1467-9701.2009.01164.x

© 2009 The AuthorJournal compilation © 2009 Blackwell Publishing Ltd, 9600 Garsington Road,Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

401

Blackwell Publishing LtdOxford, UKTWECWorld Economy0378-59201467-9701© 2009 The Author Journal compilation © Blackwell Publishers Ltd. 2009XXX Original Article

An Account of Global Intra-industry

Trade, 1962–2006

Marius Brülhart

University of Lausanne, Switzerland

1. INTRODUCTION

M

ERCHANDISE trade is by far the best documented aspect of internationaleconomic relations. Trade data therefore offer a rich source of information

on patterns and shifts in the allocation of economic activity around the globe.In this paper I describe global merchandise trade flows through the lens of

intra-industry trade (IIT) indices, which quantify the extent to which bilateralimports and exports are matched within sectors. A simple description of IITpatterns is of interest for two main purposes: as a gauge of the sectoral similarityof different national economies, and as a proxy for the intensity of factor-marketadjustment pressures associated with trade expansion.

It is easy to see how IIT can serve as an indicator of economic similarity: fortwo countries to be able to export goods of a particular sector to each other, theyboth need to produce this good.

1

Given the relative paucity of internationally

This paper is based on a background contribution to the World Bank’s 2009 World DevelopmentReport. The author would like to thank Bolormaa Tumurchudur for excellent research assistanceand Souleymane Coulibaly, Uwe Deichmann, Rob Elliott and Andreas Kopp for helpful comments.

1

The link between export values and production values is provided by export propensities, whichcan vary considerably across sectors and destinations. Hence, trade values are a noisy measure ofunderlying production values. Trade and production specialisation may even diverge. Epifani(2005), for example, develops a trade model within which increasing inter-industry specialisationin production coincides with rising IIT. The present study relies on the premise that such configura-tions are the exception, not the rule. Moreover, actual trade data occasionally (and erroneously)report goods that merely transit a country (typically one that hosts an important port) as exports.In this case, trade flows also do not reflect production patterns. Work by Amiti and Venables(2002) and by Venables et al. (2003) supports the interpretation of IIT that motivates this study.Venables et al. (2003), for example, conclude that their results ‘provide strong support for the viewthat the spatial pattern of IIT is merely reflecting the spatial distribution of country characteristics’(p. 2) and that ‘close countries do a lot of IIT because they have similar economic structures’(Abstract).

402 MARIUS BRÜLHART

© 2009 The AuthorJournal compilation © Blackwell Publishing Ltd. 2009

comparable and sectorally disaggregated production and employment data,trade-based measures can provide uniquely comprehensive (though indirect)evidence on international specialisation patterns.

The link between IIT and adjustment is similarly intuitive. If tighter inter-national trade integration leaves the sectoral composition of national economiesbroadly intact by fostering the two-way exchange of different ‘varieties’ of thesame type of good, then labour and capital do not have to be reallocated fromdeclining import-competing sectors to expanding export sectors, but simplybetween different product lines within a given sector. It is primarily because ofthis ‘smooth-adjustment hypothesis’ that the discovery of high IIT levels amongliberalising European countries in the late 1960s generated enormous interestamong policy-oriented economists and that IIT continues to be used as a diagnostictool in impact assessments of trade reforms.

2

The paper is organised as follows. Section 2 presents the IIT measuresemployed and the data on which they are computed. In Section 3, I provide asnapshot of global IIT patterns in 2006, the last year for which I have data; andin Section 4 I take a longer view by describing the evolution of IIT over the fullsample period 1962–2006. The evolution of the main cross-country determinantsof IIT, based on annual regression estimates, is described in Section 5. Section 6reports measures of marginal IIT, which are more closely related to structuraladjustment than the standard IIT indices. Section 7 concludes.

2. MEASUREMENT AND DATA

a. The Grubel–Lloyd Index

IIT is commonly understood as coterminous with the index proposed byGrubel and Lloyd (1975), which expresses IIT as a share of total bilateral tradein a particular industry

i

:

(1)

where

X

cd

,

i

and

M

cd

,

i

refer to country

c

’s exports and imports respectively, to/fromcountry

d

over one particular year (time subscripts are implied). This measure takesvalues between zero and one and increases in the share of IIT.

2

The proposition that IIT entails lower adjustment costs than inter-industry trade was originallyarticulated by Balassa (1966) and further developed in the influential monographs on IIT by Grubeland Lloyd (1975) and Greenaway and Milner (1986). For a survey, see Brülhart (1999).

GLX MX Mcd i

cd i cd i

cd i cd i,

, ,

, ,

( ),= −

−+

1| |

GLOBAL INTRA-INDUSTRY TRADE, 1962–2006 403


GL indices can be aggregated across

N

industries, as a trade-weighted (ratherthan simple arithmetic) average of the industry indices:

.

Equivalent to this definition is the following expression:

(2)

which is easily summed to give a country’s total bilateral IIT across all tradepartners:

(3)

where

D

c

is country

c

’s number of trading partners. This can be furtheraggregated across countries, for a measure of IIT by group of countries

C

(whichcould mean the entire world economy):

(4)

where

C

delineates the group of countries considered.

3

Three variants of the index in (4) will be distinguished. First, for IIT

within

aparticular country group

C

(say, among all low-income countries),

D

c

⊆

C

∀

c

.Conversely, for IIT

between

country groups (say, between low-income and

3

I let

C

symbolise both the number of countries in a particular group and the particular group (set)itself.

GL w GLX M

X M

GLX M

X Mcd cd i cd i

i

Ncd i cd i

cd i cd ii

Ni

N

cd i

cd i cd ii

N

cd i cd ii

( )

( )

, ,, ,

, ,

,

, ,

, ,

= =+

+

⎛

⎝

⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟

= −−

+=

=

=

=

=

∑∑

∑∑

1

1

1

11

| |

11

N

∑

GLX M

X Mcd

cd i cd ii

N

cd i cd ii

N

* (min , )

( )

,, ,

, ,

=+

=

=

∑

∑

21

1

GLX M

X Mc

cd i cd ii

N

cd i cd ii

Nd

Dc

* (min , )

( )

,, ,

, ,

=+

⎛

⎝

⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟

=

=

=

∑

∑∑

21

1

1

GLX M

X Mcountrygroup

cd i cd ii

N

cd i cd ii

Nd

D

c

C c

* (min , )

( )

,, ,

, ,

=+

⎛

⎝

⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟

=

=

==

∑

∑∑∑

21

1

11



high-income countries),

D

c

⊄

C

∀

c

. Finally, country group

C

’s

total

IIT (say, IITof low-income countries with all their trading partners) obtains when

D

c

⊆

{

C

,

C

′

}

∀

c

, where

C

′

denotes the complement to

C

(i.e. all trading nations that arenot part of the group

C

).Note that all these indices are computed for pairs of countries. It would be

simple to aggregate a country’s trade flows across all (or a subset) of thatcountry’s trade partners to obtain a measure of ‘multilateral IIT’. However, mostof the interest in IIT measures stems from the observation of simultaneousimports and exports between a given pair of countries, and this definition of IITalso serves best to identify similarity of trade compositions among country pairs.I therefore use bilateral IIT measures as the basis for all the results reported inthis paper.

4

The GL index is highly intuitive and has found near-universal acceptance.Two additional measurement issues nonetheless merit discussion.

(i) Categorical aggregation

The definition of an ‘industry’ is probably the most contentious issue inapplied IIT research. Grubel and Lloyd (1975, p. 86) defined IIT as ‘trade indifferentiated products which are close substitutes’. Over time, it has becomegenerally accepted that the relevant criterion is substitutability in production(rather than in consumption), since this is the aspect of industries that (a) mapstrade flows to production patterns and (b) lies at the heart of the link betweenIIT and factor-market adjustment.

5

Whilst statistical product classifications areinevitably imperfect in this respect, they are nevertheless largely guided by thecorrect criterion, i.e. an effort to group together goods with similar inputrequirements.

6

This still leaves open the question about the most appropriatelevel of statistical aggregation for the calculation of IIT indices. Whilst manyempirical studies use data at the 3-digit level, this choice is mostly motivated byexpediency rather than any

a priori

reason for favouring that level of aggrega-tion. I opt for a narrower definition in this paper, by working mainly with 5-digit

4

Through this bilateral definition, our IIT indices are conservative measures of the internationalfragmentation of production (also referred to as outward processing), as they will not capturesequential production chains that encompass more than two countries (see e.g. Hummels et al.,2001).

5

Furthermore, it is this definition of IIT that distinguishes it from trade based on comparativeadvantage and that provided the impetus for economic theorists to develop the ‘new trade theory’(see Helpman and Krugman, 1985, for a comprehensive statement).

6

In the list of five similarity criteria used by the experts in charge of the third revision of theStandard International Trade Classification (SITC), an earlier version of which my calculations arebased on, the first principle was ‘the nature of the merchandise and the materials used in itsproduction’, while ‘the uses of the product’ only ranks third (United Nations, 1986, p. viii).Evidence in favour of reasonable homogeneity of statistical sectors in terms of factor requirementshas been found by Elliott et al. (2000).



sectors and thus distinguishing up to 1,161 different ‘industries’. This minimisesthe likelihood of grouping substantially different activities under the sameindustry heading.

(ii) Adjustment for overall trade imbalance

The upper bound of a country’s mean GL index is negatively related to thesize of that country’s overall trade surplus or deficit relative to total trade. Hence,a larger imbalance in the trade account implies lower GL indices on average.Aquino (1978) has suggested a corresponding adjustment method for the GLindex. The rationale for such an adjustment has, however, been questioned on thegrounds that visible trade imbalances, both bilateral and multilateral, may wellbe compatible with balance of payments equilibrium (Greenaway and Milner,1986).

7

Given the difficulty in estimating equilibrium trade imbalances, theprofessional consensus has been to work with unadjusted GL indices. Furthermore,if IIT measures are to be interpreted as gauges of international specialisationpatterns, no modification of the basic GL index is warranted. I therefore reportunadjusted indices throughout.

b. Marginal IIT

The GL index refers to the pattern of trade in one year, and in that sense it isa static measure. This is appropriate if one seeks to quantify internationalspecialisation patterns at a particular point in time. In the context of structuraladjustment, however, it is the structure of

changes

in trade patterns which isimportant. This insight has motivated the development of ‘dynamic’ measuresreferred to as

marginal

IIT (MIIT).

8

Hamilton and Kniest (1991) first made thisdistinction by pointing out that the observation of a high proportion of IIT in oneparticular time period does not justify

a priori

any prediction of the likely patternof

change

in trade flows. Even an observed increase in static IIT between twoperiods (

GL

t

−

GL

t

−

1

> 0) could ‘hide’ a very uneven change in trade flows, con-comitant with

inter

- rather than

intra

-industry adjustment.MIIT denotes parallel increases or decreases of imports and exports in an

industry. Such matched changes of sectoral trade volumes can plausibly be

7

Egger et al. (2007) propose a similar adjustment motivated by the fact that profit repatriation ofmultinational firms can imply inherently unbalanced bilateral trade. This is an interesting extensionof IIT measurement. However, the bulk of global merchandise trade continues to be arm’s-length(OECD, 2002). Moreover, while multinational activity may cause bilateral imbalances at the sectorlevel, this is not a necessary implication.

8

The GL index is calculated on the basis of cross-border

flows

of goods and is thus not a staticmeasure in the strictest sense. Yet, ‘static’ IIT in the sense of the GL index contrasts with‘dynamic’ measures of MIIT since the latter relate to the change in these flows between twodifferent periods.



associated with a broadly neutral effect on employment. For example, if industry

i

imports expand, domestic jobs may be threatened in that industry, but if industryi exports expand by a comparable amount, this may offset lost market share inthe domestic market and yield a zero net change in the industry’s domesticemployment.9

An illustration of the difference between IIT and MIIT is given in Figure 1.Figure 1A graphs a hypothetical country’s bilateral imports and exports in aparticular industry. All points along any ray from the origin share the same GLindex, since they represent equal sectoral import–export proportions. Assumethat P represents the sectoral trade balance in the base year (t − T ): home-countryimports exceed exports by a ratio of 3:1. The industry thus exhibits a GL indexof 0.5. Assume further that the GL index is higher in the end year (t). A movefrom P to both Q1 and Q2 would show up as an increase in the GL index from0.5 to 0.8. However, the pattern of trade change is quite different between thetwo scenarios. With a shift from P to Q1, exports and imports increase at thesame absolute rate, and both countries (assuming there are only two) havecaptured an equal share of the increased volume of trade in this sector. If thispattern appears for other industries as well, then the adjustment process isintra-industry, since all countries share equally in the growth (or decline) of all

9 This conjecture evidently only holds if other relevant variables are held constant. Lovely andNelson (2000) have shown that, in general equilibrium, MIIT can be associated with inter-industryreallocation of factors if productivity is also allowed to change.

FIGURE 1IIT, MIIT and Trade Changes



these sectors. A move from P to Q2, however, implies that exports have declinedwhile imports have increased. If this pattern appears also in other industries –with the home country not necessarily always on the ‘losing’ side – the adjustmentprocess is inter-industry. A rise in the GL index can thus hide both a process ofintra- and inter-industry trade change.

Several MIIT measures have been developed to quantify the ‘matchedness’ oftrade changes. The most straightforward of these measures is a transposition ofthe Grubel–Lloyd index to first differences of sectoral trade flows (countrysubscripts implied):

(5)

where Δ stands for the difference between years t and t − T.10 This index, likethe GL index, varies between zero and one, where zero indicates marginal tradein the particular industry to be completely of the inter-industry type, and onerepresents marginal trade to be entirely of the intra-industry type.

The MIIT index is related strictly to the structure of the change in tradingpatterns – information on levels of exports or imports is not required. Hence,MIIT can be mapped onto a plane that is defined by ΔX and ΔM (Figure 1B).The possibility of such a mapping is what essentially distinguishes MIITmeasures from IIT.

The MIIT index shares most of the statistical properties of the GL index.11 Inparticular, it can also be summed across industries, by applying the followingformula for a weighted average:

(6)

and where MIITt is the weighted average of MIITit over all sectors of the economyor over all the sub-sectors of a sector.

A number of empirical studies have established significantly negative partialcorrelations between MIIT and various measures of labour market adjustmentpressures.12

10 See Brülhart (1994).11 For a detailed exploration of the parallels and differences between the IIT and MIIT indices,see Oliveras and Terra (1997).12 See Brülhart (2002) and Azhar and Elliott (2004) for discussions of the properties of this andalternative MIIT measures. Brülhart and Elliott (2002), Brülhart et al. (2006) and Cabral andSilva (2006) describe recent empirical tests of the ‘smooth adjustment hypothesis’ associated withMIIT.

MIITX M

X Mitit it

it it

,

= −−+

1| Δ Δ |

| Δ | | Δ |

MIIT w MIIT wX M

X Mt it it it

i

Nit it

it iti

N ,

( )

,= =+

+=

=

∑∑

where 1

1

| Δ | | Δ |

| Δ | | Δ |



c. Data

All trade data used for this paper are taken from the World Integrated TradeSolution (WITS) database, jointly developed by the World Bank and UNCTAD.The underlying information source is the United Nations Statistical Division’sCommodity Trade database (COMTRADE). I retain all bilateral imports andexports in value terms (current US dollars).

The definition of an ‘industry’ requires a choice not only about the level ofstatistical aggregation but also about the classification scheme to adopt. I havechosen to work with the Revision 1 version of the UN’s Standard InternationalTrade Classification (SITC). Revision 1 has the advantage of offering maximumcomparability over the sample period, as trade statistics have been recordedaccording to this classification since 1960. The disadvantage is that some sectorswhich are larger and more differentiated now than they were in 1960 are stillrecorded as a unique ‘industry’. This will imply a tendency toward highermeasured IIT in sectors that have experienced product innovation relative tosectors whose traded goods have remained unchanged. Since my main focus ison the geographic pattern of IIT rather than on sector variations, however, mypriority is to obtain consistent time series by country.

Most of my calculations are performed at the 5-digit level of the SITCclassification, which corresponds to the finest possible definition of an ‘industry’in the available data. At the 5-digit level of the SITC Revision 1, trade iscategorised into 1,161 different sectors.13 For the purpose of comparison, I alsocarry out some IIT computations at the SITC 3-digit level, where 177 sectors aredistinguished.

Although COMTRADE offers the most comprehensive available database oninternational trade flows, country coverage is not uniform between 1962 and2006. I address this issue in two ways.

One approach is to narrow down the list of countries to those for whichcoverage is broad enough such that I can be confident that intertemporalcomparisons are not driven by variations in country coverage. I have thereforeestablished a list of 56 countries which report trade data in at least 40 of the 45sample years, to produce an (almost) balanced panel of consistent data.14 I refer

13 Four examples to illustrate the narrowness of the basic industry definition: in 2006, the smallest5-digit sector was SITC 3324 (‘residual fuel oils’), accounting for 0.000002 per cent of the valueof recorded world trade; the biggest 5-digit sector was SITC 33101 (‘crude petroleum’), accountingfor 9.54 per cent of world trade; the median 5-digit sector was SITC 71965 (‘automatic vendingmachines’), accounting for 0.00014 per cent of world trade; and the mean 5-digit sector was SITC03201 (‘fish, prepared or preserved’), which accounted for 0.087 per cent of world trade.14 In the construction of the balanced panel, I also drop four of the 1,161 5-digit sectors for whichCOMTRADE does not provide consistent coverage over the sample period. The 56 countriesincluded in the ‘long coverage’ dataset are listed in the Appendix.



to this as the ‘long coverage’ dataset. For this dataset, I retain only data reportedby the importing countries, as these can be considered to be more reliable onaverage (customs services having a stronger incentive to monitor imports than tomonitor exports).

As a second approach, I exploit the fact that country coverage is broader ifone takes account of reported export data as well as of reported imports. One cantake exporting country statistics to infer imports of countries that have notsubmitted their statistics to the UN. I therefore use exporter data to fill as manygaps as possible for four sample years: 1962, 1975, 1990 and 2006. Since thenon-reporting countries are mainly from the developing world, this ‘wide coverage’dataset allows me to incorporate many low-income countries into the analysisthat are not part of the ‘long coverage’ sample.15

At the 5-digit level, the ‘long coverage’ dataset identifies between 565,000(1962) and 3,952,000 (2005) 5-digit bilateral trade flows.16 Over the 45-yearsample period, this dataset contains a total of some 39.6 million observations. Inthe ‘wide coverage’ dataset, the number of observations ranges from 962,000 in1962 to 4,903,000 in 2006. The ‘wide coverage’ data report trade flows for 177countries in 1962 and for 214 countries in 2006.17

3. GLOBAL IIT IN 2006

I begin by documenting IIT patterns in 2006, the latest available sample year.In 2006, 27 per cent of world trade was intra-industry if measured at the

5-digit level, and fully 44 per cent if measured at the 3-digit level. These are mybest estimates of the most recent IIT share, based on the 214 countries inthe ‘wide coverage’ sample, and applying the trade-weighted aggregator ofexpression (4).

At the level of individual nations, Table 1 reports trade shares and GL indices,computed according to expression (3), for the 214 sample countries. Countriesare sorted in decreasing order of their recorded share in world trade.

It becomes immediately apparent that IIT at the 3-digit level is higher than IITat the 5-digit level. The unweighted IIT averages are 0.14 at the 3-digit level and0.07 at the 5-digit level (see final row of Table 1). Table 1 also clearly shows

15 In addition to question marks over the reliability of reported export statistics, there is adefinitional inconsistency. Export values are officially measured ‘free on board’ (FOB), whereasimport values are recorded inclusive of the cost of insurance and freight (CIF). In the actual data,this seems to be a minor concern. On average, reported imports are valued about 1 per cent higherthan the corresponding exports.16 The data for 2006 were downloaded from WITS in January 2008. At that stage, coverage for2005 was still slightly larger than for 2006 (3,771,754 observations).17 See Table 1 for a list of the 214 countries in the 2006 ‘wide coverage’ dataset.

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TABLE 1Total Trade and IIT in 2006, by Country

(Sorted in decreasing order of % of world trade, ‘wide coverage’ dataset)

Country % of World Trade

% of 5-Digit Sectors Traded

GL Index, 5-Digit

GL Index, 3-Digit

World Bank Income Group*

World Bank Region

United States 13.20457 100.0 0.317 0.503 HIC North AmericaChina 9.67536 99.8 0.182 0.305 LMC Northeast AsiaGermany 9.39718 99.7 0.419 0.570 HIC Western EuropeJapan 6.29006 99.7 0.238 0.398 HIC Northeast AsiaFrance 4.46524 99.8 0.424 0.600 HIC Western EuropeUnited Kingdom 4.06561 99.8 0.362 0.525 HIC Western EuropeItaly 3.84131 99.8 0.344 0.497 HIC Western EuropeKorea, Rep. 3.21344 99.6 0.240 0.412 HIC Northeast AsiaBelgium 2.94437 99.7 0.394 0.536 HIC Western EuropeNetherlands 2.94394 99.7 0.341 0.516 HIC Western EuropeCanada 2.86110 99.7 0.421 0.599 HIC North AmericaTaiwan, China 2.77643 99.6 0.268 0.393 HIC Northeast AsiaSpain 2.25742 99.8 0.338 0.503 HIC Western EuropeMexico 2.18942 99.4 0.334 0.478 UMC Central America and CaribbeanHong Kong, China 1.92264 99.3 0.170 0.191 HIC Northeast AsiaSingapore 1.91887 99.4 0.317 0.442 HIC Southeast Asia and PacificSwitzerland 1.54548 99.6 0.396 0.561 HIC Western EuropeMalaysia 1.44576 99.4 0.294 0.466 UMC Southeast Asia and PacificIreland 1.34739 99.4 0.221 0.250 HIC Western EuropeSweden 1.17785 99.2 0.330 0.511 HIC Western EuropeAustria 1.12791 99.5 0.421 0.606 HIC Western EuropeThailand 1.11208 99.3 0.252 0.449 LMC Southeast Asia and PacificIndia 1.04446 99.3 0.127 0.318 LIC Southern AsiaRussian Federation 0.98701 99.0 0.047 0.146 UMC Eastern Europe and RussiaPoland 0.90033 99.3 0.313 0.472 UMC Eastern Europe and RussiaAustralia 0.90003 99.7 0.093 0.198 HIC Australia and New ZealandBrazil 0.86601 99.2 0.137 0.373 UMC South AmericaCzech Republic 0.76649 99.2 0.412 0.622 HIC Eastern Europe and RussiaDenmark 0.70168 99.2 0.320 0.511 HIC Western Europe

GL

OB

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TR

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Turkey 0.69206 99.1 0.130 0.217 UMC Central Asia, Caucasus and TurkeyPhilippines 0.64283 98.8 0.305 0.428 LMC Southeast Asia and PacificIndonesia 0.61715 99.7 0.117 0.291 LMC Southeast Asia and PacificHungary 0.56540 98.2 0.365 0.543 UMC Eastern Europe and RussiaFinland 0.56044 99.1 0.225 0.403 HIC Western EuropeSaudi Arabia 0.53762 99.2 0.011 0.070 HIC Western AsiaSouth Africa 0.47888 100.0 0.092 0.294 UMC Southern AfricaNorway 0.46957 99.1 0.133 0.342 HIC Western EuropePortugal 0.42791 99.3 0.292 0.485 HIC Western EuropeIsrael 0.35251 98.5 0.266 0.430 HIC Western AsiaRomania 0.34066 98.4 0.192 0.330 UMC Eastern Europe and RussiaSlovak Republic 0.32963 97.8 0.264 0.487 UMC Eastern Europe and RussiaChile 0.30348 98.1 0.025 0.095 UMC South AmericaGreece 0.28415 99.2 0.121 0.210 HIC Western EuropeArgentina 0.27734 98.3 0.156 0.313 UMC South AmericaUkraine 0.26988 98.5 0.115 0.274 LMC Eastern Europe and RussiaVenezuela 0.17910 96.7 0.024 0.175 UMC South AmericaColombia 0.17831 97.9 0.082 0.145 LMC South AmericaNew Zealand 0.17333 98.8 0.133 0.298 HIC Australia and New ZealandSlovenia 0.16968 98.7 0.317 0.523 HIC Western EuropeUnited Arab Emirates 0.16872 99.4 0.000 0.060 HIC Western AsiaVietnam 0.16209 98.0 0.000 0.077 LIC Southeast Asia and PacificPakistan 0.15677 97.9 0.018 0.087 LIC Southern AsiaMorocco 0.14107 97.5 0.091 0.150 LMC Northern AfricaIran, Islamic Rep. 0.13437 97.2 0.007 0.106 LMC Western AsiaKazakhstan 0.13204 95.9 0.042 0.081 UMC Central Asia, Caucasus and TurkeyBulgaria 0.13088 98.1 0.140 0.287 UMC Eastern Europe and RussiaLuxembourg 0.13031 98.4 0.245 0.407 HIC Western EuropeCosta Rica 0.11561 95.3 0.123 0.212 UMC Central America and CaribbeanBangladesh 0.11347 92.6 0.000 0.016 LIC Southern AsiaCroatia 0.10968 97.5 0.195 0.306 UMC Eastern Europe and RussiaAlgeria 0.10638 95.1 0.004 0.026 LMC Northern AfricaPeru 0.10586 97.4 0.025 0.066 LMC South AmericaEgypt, Arab Rep. 0.10025 98.5 0.030 0.107 LMC Northern Africa



GL Index, 5-Digit

GL Index, 3-Digit


World Bank Region

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Lithuania 0.09327 97.2 0.147 0.256 UMC Eastern Europe and RussiaQatar 0.09289 96.2 0.007 0.030 HIC Western AsiaBelarus 0.07765 95.6 0.042 0.157 LMC Eastern Europe and RussiaEstonia 0.06955 96.5 0.211 0.336 HIC Eastern Europe and RussiaYugoslavia 0.06526 97.2 0.110 0.222 UMC Eastern Europe and RussiaNigeria 0.06394 93.5 0.000 0.013 LIC Western AfricaTrinidad and Tobago 0.06235 93.4 0.012 0.025 HIC Central America and CaribbeanGuatemala 0.06132 96.2 0.067 0.103 LMC Central America and CaribbeanOman 0.06004 95.6 0.006 0.032 UMC Western AsiaTunisia 0.05985 93.7 0.000 0.072 LMC Northern AfricaEcuador 0.05934 95.9 0.047 0.123 LMC South AmericaLatvia 0.05596 96.6 0.173 0.291 UMC Eastern Europe and RussiaJordan 0.05395 94.5 0.023 0.063 LMC Western AsiaKuwait 0.05217 93.1 0.000 0.028 HIC Western AsiaSri Lanka 0.05134 94.0 0.000 0.045 LMC Southern AsiaHonduras 0.04734 93.2 0.040 0.052 LMC Central America and CaribbeanDominican Republic 0.04128 93.1 0.000 0.045 LMC Central America and CaribbeanEl Salvador 0.04093 94.0 0.067 0.112 LMC Central America and CaribbeanSyrian Arab Republic 0.03846 96.4 0.014 0.048 LMC Western AsiaCyprus 0.03791 95.9 0.101 0.225 HIC Western EuropeMacao 0.03753 89.6 0.090 0.144 HIC Northeast AsiaIraq 0.03596 81.7 0.000 0.008 LMC Western AsiaMalta 0.03585 93.9 0.244 0.390 HIC Western EuropeBosnia and Herzegovina 0.03554 96.2 0.140 0.277 LMC Eastern Europe and RussiaAngola 0.03444 93.7 0.000 0.007 LMC Middle AfricaSudan 0.03213 92.6 0.002 0.009 LIC Eastern AfricaLibya 0.03212 82.9 0.000 0.015 UMC Northern AfricaPanama 0.02883 94.7 0.047 0.116 UMC Central America and CaribbeanCambodia 0.02862 78.2 0.000 0.006 LIC Southeast Asia and PacificIceland 0.02816 95.0 0.039 0.097 HIC Western EuropeBahrain 0.02506 93.8 0.027 0.084 HIC Western Asia



GL Index, 5-Digit

GL Index, 3-Digit


World Bank Region

TABLE 1 Continued

GL

OB

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Uruguay 0.02427 94.0 0.072 0.175 UMC South AmericaJamaica 0.02388 91.7 0.022 0.086 LMC Central America and CaribbeanAzerbaijan 0.02192 88.6 0.011 0.041 LMC Central Asia, Caucasus and TurkeyCôte d’Ivoire 0.02164 90.9 0.005 0.022 LIC Western AfricaGhana 0.02142 95.9 0.008 0.016 LIC Western AfricaParaguay 0.02089 88.3 0.024 0.054 LMC South AmericaMauritius 0.01912 94.0 0.058 0.079 UMC Eastern AfricaMacedonia, FYR 0.01906 93.4 0.071 0.132 LMC Eastern Europe and RussiaNicaragua 0.01847 90.5 0.022 0.038 LMC Central America and CaribbeanKenya 0.01757 94.2 0.000 0.033 LIC Eastern AfricaZambia 0.01737 96.5 0.008 0.016 LIC Eastern AfricaYemen 0.01631 91.5 0.003 0.011 LIC Western AsiaEthiopia (excludes Eritrea) 0.01629 94.6 0.040 0.036 LIC Eastern AfricaBotswana 0.01622 97.7 0.012 0.007 UMC Southern AfricaCuba 0.01546 85.9 0.000 0.015 LMC Central America and CaribbeanBolivia 0.01516 94.0 0.012 0.050 LMC South AmericaNamibia 0.01431 97.6 0.003 0.008 LMC Southern AfricaUzbekistan 0.01428 82.3 0.000 0.062 LIC Central Asia, Caucasus and TurkeyTanzania 0.01335 96.6 0.009 0.017 LIC Eastern AfricaBrunei 0.01313 90.5 0.003 0.025 HIC Southeast Asia and PacificLebanon 0.01311 92.7 0.000 0.063 UMC Western AsiaMyanmar 0.01268 86.8 0.000 0.019 LIC Southeast Asia and PacificAlbania 0.01251 91.7 0.139 0.268 LMC Eastern Europe and RussiaMoldova 0.01192 90.2 0.062 0.166 LMC Eastern Europe and RussiaGeorgia 0.01131 92.5 0.020 0.062 LMC Central Asia, Caucasus and TurkeyMadagascar 0.01113 91.1 0.017 0.024 LIC Eastern AfricaCameroon 0.01094 89.3 0.004 0.023 LMC Middle AfricaMozambique 0.01010 94.0 0.009 0.031 LIC Eastern AfricaSenegal 0.00960 90.8 0.014 0.045 LIC Western AfricaBahamas, The 0.00959 81.5 0.000 0.022 HIC Central America and CaribbeanMongolia 0.00924 88.7 0.008 0.024 LIC Northeast AsiaGabon 0.00885 86.2 0.003 0.009 UMC Middle AfricaKorea, Dem. Rep. 0.00817 87.6 0.000 0.039 LIC Northeast AsiaCongo, Rep. 0.00806 80.9 0.000 0.009 LMC Middle AfricaNew Caledonia 0.00768 89.6 0.009 0.032 HIC Southeast Asia and Pacific



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Benin 0.00732 71.8 0.000 0.001 LIC Western AfricaZimbabwe 0.00717 94.0 0.000 0.037 LIC Eastern AfricaUganda 0.00689 93.5 0.004 0.012 LIC Eastern AfricaEquatorial Guinea 0.00667 63.5 0.000 0.009 UMC Middle AfricaNetherlands Antilles 0.00658 83.2 0.000 0.036 HIC Central America and CaribbeanTurkmenistan 0.00607 71.1 0.000 0.012 LMC Central Asia, Caucasus and TurkeyFiji 0.00573 91.2 0.036 0.092 LMC Southeast Asia and PacificHaiti 0.00570 69.1 0.000 0.037 LIC Central America and CaribbeanKyrgyz Republic 0.00551 88.8 0.031 0.076 LIC Central Asia, Caucasus and TurkeyArmenia 0.00543 87.3 0.140 0.133 LMC Central Asia, Caucasus and TurkeyBarbados 0.00518 91.2 0.046 0.090 HIC Central America and CaribbeanFrench Polynesia 0.00498 87.5 0.013 0.022 HIC Southeast Asia and PacificLiberia 0.00497 66.1 0.000 0.001 LIC Western AfricaAfghanistan 0.00470 75.6 0.000 0.012 LIC Southern AsiaPapua New Guinea 0.00453 79.6 0.000 0.040 LIC Southeast Asia and PacificCongo, Dem. Rep. 0.00451 85.8 0.000 0.011 LIC Middle AfricaNepal 0.00447 89.1 0.000 0.161 LIC Southern AsiaCayman Islands 0.00420 67.7 0.000 0.009 HIC Central America and CaribbeanMalawi 0.00409 88.6 0.027 0.034 LIC Eastern AfricaTogo 0.00407 74.5 0.000 0.005 LIC Western AfricaLao PDR 0.00365 77.1 0.000 0.016 LIC Southeast Asia and PacificAruba 0.00335 74.8 0.000 0.010 HIC Central America and CaribbeanBermuda 0.00313 66.5 0.000 0.013 HIC North AmericaTajikistan 0.00311 69.9 0.000 0.017 LIC Central Asia, Caucasus and TurkeyFaeroe Islands 0.00300 89.7 0.047 0.063 HIC Western EuropeGuyana 0.00292 86.5 0.014 0.045 LMC South AmericaMauritania 0.00281 73.0 0.001 0.008 LIC Western AfricaGuinea 0.00273 72.5 0.000 0.011 LIC Western AfricaMaldives 0.00269 81.5 0.005 0.009 LMC Southern AsiaSuriname 0.00244 74.5 0.000 0.227 LMC South AmericaDjibouti 0.00238 72.9 0.000 0.036 LMC Western Asia



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British Virgin Islands 0.00218 63.8 0.000 0.024 n.a. Central America and CaribbeanBelize 0.00216 83.5 0.015 0.056 UMC Central America and CaribbeanMarshall Islands 0.00214 49.4 0.000 0.003 LMC Southeast Asia and PacificMali 0.00212 75.6 0.000 0.023 LIC Western AfricaSeychelles 0.00191 87.4 0.085 0.121 n.a. n.a.Cape Verde 0.00186 82.9 0.013 0.034 LMC Western AfricaChad 0.00185 55.1 0.000 0.003 LIC Middle AfricaLesotho 0.00172 38.2 0.000 0.001 LMC Southern AfricaSwaziland 0.00171 63.6 0.000 0.021 LMC Southern AfricaBurkina Faso 0.00143 65.2 0.000 0.008 LIC Western AfricaAndorra 0.00137 74.4 0.000 0.128 HIC Western EuropeGreenland 0.00128 81.2 0.000 0.028 HIC North AmericaAntigua and Barbuda 0.00119 72.5 0.000 0.011 HIC Central America and CaribbeanGibraltar 0.00106 77.0 0.000 0.038 n.a. Western EuropeNiger 0.00097 64.4 0.000 0.021 LIC Western AfricaSt. Vincent and

the Grenadines0.00092 80.4 0.007 0.020 UMC Central America and Caribbean

St. Kitts and Nevis 0.00086 80.5 0.108 0.096 UMC Central America and CaribbeanTurks and Caicos Isl. 0.00073 58.2 0.000 0.005 n.a. Central America and CaribbeanSierra Leone 0.00073 66.6 0.000 0.066 LIC Western AfricaSt. Lucia 0.00072 65.9 0.000 0.061 UMC Central America and CaribbeanGambia, The 0.00072 76.9 0.003 0.009 LIC Western AfricaDominica 0.00063 77.0 0.019 0.058 UMC Central America and CaribbeanRwanda 0.00060 62.0 0.000 0.007 LIC Eastern AfricaGuam 0.00055 54.9 0.000 0.051 HIC Southeast Asia and PacificSamoa 0.00043 63.0 0.000 0.042 LMC Southeast Asia and PacificSomalia 0.00040 43.6 0.000 0.036 LIC Eastern AfricaEritrea 0.00040 54.1 0.000 0.027 LIC Eastern AfricaVanuatu 0.00036 63.0 0.000 0.018 LMC Southeast Asia and PacificBhutan 0.00036 38.0 0.000 0.092 LMC Southern AsiaGrenada 0.00035 66.5 0.000 0.018 UMC Central America and CaribbeanSolomon Islands 0.00035 57.2 0.000 0.005 LIC Southeast Asia and PacificBurundi 0.00027 53.9 0.000 0.065 LIC Eastern AfricaTokelau 0.00024 38.7 0.000 0.032 n.a. Southeast Asia and PacificCentral African Republic 0.00023 50.9 0.000 0.025 LIC Middle Africa



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Cook Islands 0.00020 65.4 0.000 0.039 n.a. Southeast Asia and PacificFalkland Islands 0.00020 32.4 0.000 0.020 n.a. South AmericaGuinea-Bissau 0.00020 55.5 0.000 0.023 LIC Western AfricaSão Tomé and Principe 0.00018 65.6 0.006 0.077 LIC Middle AfricaComoros 0.00017 49.2 0.000 0.029 LIC Eastern AfricaTonga 0.00016 60.6 0.000 0.032 LMC Southeast Asia and PacificSaint Pierre and Miquelon 0.00015 48.6 0.000 0.012 n.a. North AmericaMicronesia, Fed. Sts. 0.00015 46.4 0.000 0.004 LMC Southeast Asia and PacificAnguila 0.00013 45.1 0.000 0.010 n.a. Central America and CaribbeanNorthern Mariana Islands 0.00012 38.7 0.000 0.040 UMC Southeast Asia and PacificWallis and Futura Isl. 0.00010 60.2 0.002 0.010 n.a. Southeast Asia and PacificPalau 0.00010 45.8 0.000 0.018 UMC Southeast Asia and PacificEast Timor 0.00009 34.3 0.000 0.005 LIC Southeast Asia and PacificSaint Helena 0.00007 50.5 0.000 0.023 n.a. Western AfricaMontserrat 0.00007 57.3 0.033 0.095 n.a. n.a.Kiribati 0.00006 47.5 0.000 0.011 LMC Southeast Asia and PacificTuvalu 0.00004 40.2 0.000 0.004 n.a. Southeast Asia and PacificNiue 0.00003 37.2 0.000 0.029 n.a. Southeast Asia and PacificNauru 0.00003 29.3 0.000 0.067 n.a. Southeast Asia and PacificPitcairn 0.00002 18.6 0.000 0.002 n.a. Southeast Asia and PacificUnweighted average 0.464 83.3 0.073 0.138 n.a. n.a.

* Taken from World Bank (2006, p. 287).



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Table 1 Continued



that large trading nations tend to exhibit higher IIT, which explains why theseunweighted averages are significantly smaller than the aggregate IIT sharesreported above. It suffices to look at the third and fourth data columns to realisethat GL indices tend to increase with the size of countries’ trade. The simplecorrelation coefficients between trade shares and GL indices are 0.58 (3-digit)and 0.52 (5-digit).

Furthermore, the second data column of Table 1 shows that larger tradingcountries also tend to trade in a broader set of industries. France is the countrywith the highest level of IIT at the 5-digit level (0.424), whereas at the 3-digitlevel the highest level of IIT is recorded by the Czech Republic (0.622). At theopposite end of the list, a full 85 sample countries do not engage in any discernibleIIT at the 5-digit level. The largest of these 85 countries, in terms of its share inrecorded world trade, is the United Arab Emirates. At the 3-digit level, however,all countries exhibit some IIT, with the lowest GL index of 0.001 observed forBenin, Lesotho and Liberia.

While average IIT shares differ significantly, variations across countries arevery similar for the two levels of sectoral aggregation: the correlation coefficientacross the 216 countries between the 3-digit and the 5-digit GL indices is 0.97.

In Table 2, I slice the global trade matrix by sector rather than country, and Ipresent trade shares as well as 5-digit and 3-digit GL indices separately for the177 3-digit sectors. Again one can easily observe that 3-digit GL indices arehigher than 5-digit GL indices (aggregated to the 3-digit level), the unweightedaverages corresponding to 0.28 and 0.21, respectively. And at 0.92, the correlationbetween the two sets of GL indices is again very high. Sectoral disaggregationthus strongly affects observed average levels of IIT, but it is of secondary impor-tance in a description of broad cross-sectional patterns.

The 3-digit sector with the highest level of observed 5-digit IIT (GL = 0.527)is ‘Electric Power Machinery and Switchgear’, whereas the only 3-digit sectorfor which I find a 5-digit GL index of 0.000 is ‘Concentrated Uranium andThorium Ore’.

Figure 2 shows IIT by country income groups, taking the World Bank’s (2006)categorisation and applying the ‘within’ version of the group-level GL indexdefined in expression (4). Trade among high-income countries is characterised bythe highest IIT shares on average. IIT among the low-income countries, in con-trast, is virtually non-existent. Strikingly, however, the highest 5-digit IIT levelis observed for trade among lower-middle-income countries – higher even thanfor trade among high-income economies. There are good reasons to believe thatthe high IIT among lower-middle-income countries is due to processing trade invertically fragmented industries (the four main trading nations in this categoryare China, Thailand, the Philippines and Indonesia; see Table 1).

Finally, Figure 3 reports summary IIT according to a classification of 5-digitsectors by the three main stages of the production chain: primary, intermediate



FIGURE 2IIT by Income Group, 2006

Notes: Country grouping according to World Bank categorisation (see Table 1); ‘wide coverage’ dataset.

FIGURE 3IIT by Product Group, 2006

Notes: Product grouping according to United Nations ‘Broad Economic Categories’; ‘wide coverage’ dataset.

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TABLE 2Total Trade and IIT in 2006, by 3-Digit Industry


Sector Name SITC 3-Digit Code

% of World Trade

Number of Sample Countries Trading

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BEC Product Grouping*

MACHINES NES NONELECTRIC 719 14.58087 233 0.423 0.554 IntermediateELECTRICAL MACHINERY NES 729 10.49781 233 0.431 0.538 IntermediateORGANIC CHEMICALS 512 10.25057 231 0.277 0.499 IntermediateROAD MOTOR VEHICLES 732 7.55329 233 0.407 0.484 FinalINSTRUMENTS, APPARATUS 861 6.97463 231 0.364 0.520 IntermediateCLOTHING NOT OF FUR 841 6.05836 233 0.119 0.142 FinalMEDICINAL ETC PRODUCTS 541 2.87797 228 0.403 0.510 IntermediateCHEMICALS NES 599 2.70815 233 0.394 0.559 IntermediateOFFICE MACHINES 714 2.32375 233 0.269 0.305 IntermediateTELECOMMUNICATIONS EQUIP 724 2.19387 233 0.237 0.288 IntermediateMETAL MANUFACTURES NES 698 1.83187 233 0.426 0.554 IntermediatePLASTIC MATERIALS ETC 581 1.65085 231 0.458 0.516 IntermediatePOWER MACHINERY NON-ELEC 711 1.62557 231 0.499 0.656 IntermediateSOUND RECORDERS, PRODUCRS 891 1.36538 233 0.234 0.292 FinalMACHS FOR SPCL INDUSTRYS 718 1.33521 229 0.294 0.364 IntermediateOTHER MANUFACTURED GOODS 899 1.28000 232 0.258 0.411 FinalIRN, STL UNIV, PLATE, SHEET 674 1.11075 225 0.254 0.415 IntermediateINORG ELEMNTS, OXIDES, ETC 513 1.01977 225 0.142 0.451 IntermediateCRUDE PETROLEUM, ETC 331 0.99246 174 0.010 0.010 PrimaryPAPER AND PAPERBOARD 641 0.96192 228 0.294 0.439 IntermediateTOYS, SPORTING GOODS, ETC 894 0.90346 230 0.125 0.169 FinalCOPPER 682 0.78289 224 0.150 0.295 IntermediateTEXTILE YARN AND THREAD 651 0.73323 229 0.267 0.493 IntermediateWOVEN TEXTILES NONCOTTON 653 0.69255 229 0.225 0.317 IntermediateALUMINIUM 684 0.63360 226 0.234 0.381 IntermediatePRINTED MATTER 892 0.52267 232 0.414 0.509 FinalFRUIT FRSH NUTS FRSH DRY 51 0.50597 231 0.060 0.168 PrimaryELEC PWR MACH, SWITCHGEAR 722 0.50188 232 0.527 0.566 Intermediate

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IRON, STL PRIMARY FORMS 672 0.48074 218 0.162 0.339 IntermediateOTHR INORGANIC CHEMICALS 514 0.47668 227 0.178 0.472 IntermediateIRON AND STEEL SHAPES 673 0.46709 229 0.274 0.423 IntermediateFURNITURE 821 0.43806 231 0.248 0.271 FinalNONFER BASE MTL ORE, CONC 283 0.41198 194 0.012 0.091 PrimarySPECIAL TEXTILE ETC PROD 655 0.40117 229 0.355 0.531 IntermediateAIRCRAFT 734 0.36833 225 0.243 0.306 FinalMEAT FRESH, CHILLD, FROZEN 11 0.36792 231 0.140 0.255 PrimaryVEG ETC FRSH, SMPLY PRSVD 54 0.33378 231 0.175 0.305 PrimaryGLASS 664 0.33042 229 0.329 0.528 IntermediateDOMESTIC ELECTRIC EQUIP 725 0.32606 231 0.195 0.245 FinalRUBBER ARTICLES NES 629 0.32132 233 0.414 0.477 IntermediateGAS NATURAL AND MANUFCTD 341 0.28634 219 0.055 0.072 PrimaryPEARL, PREC-, SEMI-P STONE 667 0.27258 208 0.315 0.342 PrimaryFOOTWEAR 851 0.27214 230 0.097 0.102 FinalALCOHOLIC BEVERAGES 112 0.26754 230 0.122 0.294 FinalELECTR DISTRIBUTING MACH 723 0.26685 232 0.453 0.504 IntermediatePIGMENTS, PAINTS, ETC 533 0.25725 230 0.344 0.445 IntermediateWATCHES AND CLOCKS 864 0.25272 227 0.164 0.238 IntermediateCRUDE VEG MATERIALS NES 292 0.24167 230 0.192 0.310 PrimaryCOAL, COKE, BRIQUETTES 321 0.22438 207 0.017 0.051 PrimaryPULP AND WASTE PAPER 251 0.22071 200 0.067 0.133 IntermediateTOOLS 695 0.21890 232 0.355 0.433 IntermediateANIMAL FEEDING STUFF 81 0.21698 225 0.185 0.333 PrimaryPLUMBG, HEATNG, LGHTNG EQU 812 0.21507 231 0.266 0.341 IntermediateIRON, STL TUBES, PIPES, ETC 678 0.21431 231 0.293 0.396 IntermediateOTH NONMETAL MINERAL MFS 663 0.21123 228 0.323 0.553 IntermediateFOOD PREPARATIONS NES 99 0.20948 231 0.386 0.488 FinalWOOD MANUFACTURES NES 632 0.20583 230 0.235 0.286 IntermediateAGRICULTURAL MACHINERY 712 0.20296 229 0.317 0.411 IntermediateNON-FERROUS METAL SCRAP 284 0.20261 212 0.235 0.345 Primary


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SHIPS AND BOATS 735 0.20260 227 0.099 0.210 IntermediateCEREAL ETC PREPARATIONS 48 0.19850 229 0.367 0.542 FinalFISH FRESH, SIMPLY PRESVD 31 0.18530 230 0.173 0.198 PrimaryOTHER CRUDE MINERALS 276 0.17500 227 0.136 0.416 PrimaryARTICLES OF PAPER ETC 642 0.17380 230 0.413 0.522 IntermediateMETALWORKING MACHINERY 715 0.17359 224 0.293 0.324 IntermediateTEXTILE, LEATHER MACHNRY 717 0.17231 229 0.205 0.275 IntermediateGOLD, SILVER WARE, JEWELRY 897 0.17180 228 0.228 0.275 FinalWOOD SHAPED 243 0.16564 228 0.102 0.180 IntermediateCEMENT ETC BUILDING PROD 661 0.15198 227 0.095 0.192 IntermediateVENEERS, PLYWOOD, ETC 631 0.13524 226 0.160 0.270 IntermediatePIG IRON ETC 671 0.13514 198 0.085 0.168 IntermediateFRUIT PRESERVED, PREPARED 53 0.13487 231 0.211 0.289 IntermediateCLAY, REFRACTORY BLDG PRD 662 0.12961 227 0.118 0.213 IntermediateROAD VEHICLES NON-MOTOR 733 0.12760 231 0.333 0.390 FinalPETROLEUM PRODUCTS 332 0.12514 227 0.174 0.362 IntermediateOIL SEEDS, NUTS, KERNELS 221 0.12218 216 0.040 0.078 PrimaryLEATHER 611 0.12183 206 0.161 0.221 IntermediateTEXTILE ETC PRODUCTS NES 656 0.10784 232 0.127 0.155 FinalBASE MTL HOUSEHOLD EQUIP 697 0.10740 230 0.158 0.202 FinalRAILWAY VEHICLES 731 0.10645 217 0.275 0.458 FinalPHOTO, CINEMA SUPPLIES 862 0.10582 222 0.217 0.287 IntermediateSILVER, PLATINUM, ETC 681 0.10513 182 0.133 0.257 IntermediateFERTILISERS MANUFACTURED 561 0.10375 219 0.059 0.142 IntermediateCOTTON FABRICS, WOVEN 652 0.10151 227 0.217 0.298 IntermediateOFFICE SUPPLIES NES 895 0.10115 230 0.209 0.314 IntermediateRUBBER CRUDE, SYNTHETIC 231 0.09701 216 0.198 0.284 PrimaryARTICLES OF PLASTIC NES 893 0.09527 233 0.509 0.509 FinalGLASSWARE 665 0.08910 228 0.247 0.368 IntermediateSUGAR AND HONEY 61 0.08771 228 0.114 0.232 IntermediateNON-FER BASE METALS NES 689 0.08503 205 0.372 0.489 IntermediateWORKS OF ART ETC 896 0.08197 223 0.413 0.504 FinalNICKEL 683 0.07833 186 0.092 0.138 IntermediateFIXED VEG OILS, SOFT 421 0.07682 226 0.106 0.238 IntermediateSTL, COPPR NAILS, NUTS, ETC 694 0.07502 233 0.358 0.385 IntermediateFIXED VEG OIL NONSOFT 422 0.07343 224 0.034 0.069 Intermediate


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MATERIALS OF RUBBER 621 0.07071 228 0.419 0.540 IntermediateSOAPS, CLEANING ETC PREPS 554 0.06767 231 0.434 0.490 IntermediateMETAL TANKS, BOXES, ETC 692 0.06410 228 0.343 0.483 IntermediateVEGTBLES ETC PRSVD, PREPD 55 0.06246 228 0.201 0.274 IntermediateIRON ORE, CONCENTRATES 281 0.05800 144 0.017 0.026 PrimarySYNTHETIC, REGENRTD FIBRE 266 0.05745 205 0.149 0.304 IntermediateFLOOR COVR, TAPESTRY ETC 657 0.05695 226 0.197 0.236 FinalSTRUCTURES AND PARTS NES 691 0.05617 230 0.353 0.374 IntermediateMILK AND CREAM 22 0.05566 227 0.229 0.277 IntermediateTOBACCO MFRS 122 0.05352 225 0.108 0.176 FinalWIRE PRODUCTS NON ELECTR 693 0.05306 231 0.260 0.399 IntermediateELECTRO-MEDCL, XRAY EQUIP 726 0.05262 225 0.477 0.540 IntermediateLIVE ANIMALS 1 0.05191 209 0.155 0.251 PrimarySTONE, SAND AND GRAVEL 273 0.05091 224 0.136 0.290 PrimaryZINC 686 0.05058 204 0.104 0.148 IntermediatePERFUME, COSMETICS, ETC 553 0.04267 232 0.402 0.402 FinalCRUDE ANIMAL MATTER NES 291 0.03817 218 0.242 0.391 PrimaryCUTLERY 696 0.03685 228 0.148 0.213 FinalLACE, RIBBONS, TULLE, ETC 654 0.03566 227 0.199 0.275 IntermediateRADIOACTIVE ETC MATERIAL 515 0.03534 177 0.206 0.238 IntermediateCOTTON 263 0.03397 199 0.008 0.017 PrimaryCOCOA 72 0.03225 204 0.033 0.053 IntermediateCOFFEE 71 0.03217 225 0.112 0.139 IntermediateMEAT TINNED NES OR PREPD 13 0.03150 225 0.264 0.298 FinalHIDES, SKINS, UNDRESSED 211 0.03135 193 0.070 0.103 PrimaryWOOD ROUGH 242 0.03086 216 0.090 0.144 PrimaryWOOL AND ANIMAL HAIR 262 0.03035 171 0.059 0.126 PrimaryIRON AND STEEL SCRAP 282 0.03016 214 0.170 0.170 PrimaryTRAVEL GOODS, HANDBAGS 831 0.02989 229 0.110 0.110 FinalFISH ETC TINNED, PREPARED 32 0.02814 225 0.102 0.123 Final


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ELECTRIC ENERGY 351 0.02804 107 0.259 0.259 IntermediateESSENTL OIL, PERFUME, ETC 551 0.02802 221 0.184 0.252 IntermediateLEATHER ETC MANUFACTURES 612 0.02736 218 0.339 0.395 FinalPROCESD ANML VEG OIL, ETC 431 0.02644 212 0.188 0.297 IntermediateWAR FIREARMS, AMMUNITION 951 0.02587 203 0.136 0.206 FinalIRN, STL WIRE EXCL W ROD 677 0.02305 222 0.337 0.408 IntermediateWHEAT ETC UNMILLED 41 0.02286 189 0.023 0.023 PrimaryNON-ALC BEVERAGES NES 111 0.02186 226 0.253 0.310 FinalCOAL, PETROLEUM ETC CHEMS 521 0.02122 196 0.283 0.328 IntermediatePOTTERY 666 0.02017 228 0.095 0.117 FinalSYNT DYE, NAT INDGO, LAKES 531 0.01991 223 0.414 0.437 IntermediateSPICES 75 0.01651 227 0.080 0.150 PrimaryCHEESE AND CURD 24 0.01648 226 0.301 0.301 FinalRICE 42 0.01597 224 0.015 0.022 IntermediateLEAD 685 0.01439 190 0.090 0.145 IntermediateIRN, STL CASTINGS UNWORKE 679 0.01421 221 0.336 0.409 IntermediateMAIZE UNMILLED 44 0.01417 208 0.039 0.039 PrimaryTIN 687 0.01328 183 0.082 0.168 IntermediateSILVER AND PLATINUM ORES 285 0.01294 158 0.176 0.220 PrimaryCHOCOLATE AND PRODUCTS 73 0.01250 225 0.413 0.413 FinalDRIED FRUIT 52 0.01007 221 0.065 0.107 PrimaryANIMAL OILS AND FATS 411 0.00940 201 0.115 0.268 IntermediateEXPLOSIVES, PYROTECH PROD 571 0.00826 216 0.124 0.290 IntermediateTOBACCO UNMFD 121 0.00703 190 0.064 0.064 PrimaryRAILWY RAILS ETC IRN, STL 676 0.00517 196 0.151 0.182 IntermediateCEREALS NES UNMILLED 45 0.00466 201 0.050 0.084 PrimaryMEAT DRIED, SALTED, SMOKED 12 0.00456 209 0.182 0.208 FinalMARGARINE, SHORTENING 91 0.00455 220 0.191 0.256 FinalFERTILISERS, CRUDE 271 0.00454 194 0.033 0.108 PrimaryNATURAL ABRASIVES 275 0.00440 204 0.151 0.295 PrimaryBUTTER 23 0.00423 222 0.171 0.171 FinalTEA AND MATE 74 0.00419 225 0.046 0.061 PrimaryDYES NES, TANNING PRODS 532 0.00410 197 0.184 0.297 IntermediateBARLEY UNMILLED 43 0.00401 154 0.045 0.045 PrimaryWHEAT ETC MEAL OR FLOUR 46 0.00352 226 0.115 0.165 IntermediateWASTE OF TEXTILE FABRICS 267 0.00330 224 0.087 0.183 Primary


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FUEL WOOD AND CHARCOAL 241 0.00315 200 0.149 0.179 PrimaryFUR ETC CLOTHES, PROD 842 0.00312 182 0.102 0.114 FinalCORK MANUFACTURES 633 0.00281 209 0.120 0.130 IntermediateSULPHUR ETC 274 0.00277 160 0.025 0.042 PrimaryEGGS 25 0.00242 217 0.218 0.218 PrimaryVEG FIBRE, EXCL COTN JUTE 265 0.00226 182 0.090 0.145 PrimaryFUR SKINS UNDRESSED 212 0.00198 136 0.113 0.113 PrimaryFUR SKINS TANNED, DRESSED 613 0.00171 152 0.200 0.200 IntermediateMEAL AND FLOUR NON-WHEAT 47 0.00128 217 0.155 0.245 IntermediateSILK 261 0.00094 130 0.009 0.017 PrimaryZOO ANIMALS, PETS 941 0.00066 199 0.219 0.219 PrimaryURANIUM, THORIUM ORE, CONC 286 0.00053 39 0.000 0.000 PrimaryCORK RAW AND WASTE 244 0.00042 147 0.326 0.345 PrimaryCOIN NONGOLD, NONCURRENT 961 0.00019 170 0.151 0.151 FinalJUTE 264 0.00014 147 0.009 0.009 PrimaryURANIUM, THORIUM, ALLOYS 688 0.00003 89 0.252 0.252 IntermediateUnweighted average n.a. 0.56497 214 0.205 0.282 n.a.

* Product grouping according to United Nations ‘Broad Economic Categories’; most prevalent (unweighted) 5-digit group within each 3-digit sector.


% of World Trade


GL Index, 5-Digit

GL Index, 3-Digit


Table 2 Continued



and final goods.18 Not surprisingly, primary goods are found to exhibit by far thelowest average IIT. It is interesting, however, to observe that average IIT inintermediate goods is considerably higher than IIT in final goods. This againsuggests that vertical fragmentation of production processes across countryborders might be as important (or even more important) in explaining global IITpatterns as international product differentiation and consumer tastes for variety.

4. THE EVOLUTION OF GLOBAL IIT, 1962–2006

a. Aggregate IIT

I now turn to the description of changes in IIT over time, based on the ‘longcoverage’ sample, which offers comparable data over the full sample period.Figure 4 provides the main picture. It shows how, irrespective of the level of

18 The classification at the 5-digit level is taken from the United Nations’ Broad EconomicCategories, concorded to the SITC, Rev. 1. Table 2 shows this grouping at the 3-digit level. Thefull (5-digit) classification can be provided on request.

FIGURE 4Evolution of Global IIT, 1962–2006 (‘Long Coverage’ Sample)



categorical aggregation, global IIT has exhibited a secular upward trend thatlevelled out in the mid-1990s.19 In this narrower country sample, more than athird of global trade is now IIT if measured at the 5-digit level, and more thanhalf if measured at the 3-digit level. The upward trend in IIT suggests a processof worldwide structural convergence: economies are becoming more similar overtime in terms of their sectoral compositions.

As a complement to the time series of Figure 4, which is based on data for the46 predominantly higher-income countries for which consistent import data areavailable, I show aggregate IIT levels for the ‘wide coverage’ dataset in Figure 5.It is unsurprising that IIT shares are lower in Figure 5 than in Figure 4, as thelatter omits most low-income countries. Nonetheless, the broadly increasingshare of IIT in world trade is as evident in Figure 5 as in Figure 4. Since the‘wide coverage’ dataset is my most comprehensive sample, it provides mypreferred estimates for the current (i.e. 2006) shares of IIT in world trade: 27per cent if measured at the 5-digit level, and 44 per cent if measured at the3-digit level.

19 Measured IIT in 2004 and 2005 is somewhat biased downward due to the fact that in those yearsCOMTRADE data attribute a significant share of EU imports to the EU as a whole rather than tothe individual destination countries. This reduces observed import volumes of EU member statesin those two years.

FIGURE 5Global IIT in 1962, 1975, 1990 and 2006 (‘Wide Coverage’ Sample)



b. IIT by Sector

Figure 6 illustrates that the rise in global IIT has been broadly shared acrosssectors. Over our sample period, the average 5-digit GL index has increased innine out of the ten 1-digit sectors. The only exception is the Mineral Fuels sector(SITC sector 3), where, for obvious reasons, inter-industry trade has remainedhighly dominant. Proportionally the largest rise in IIT is observed in the ‘Foodand Live Animals’ sector (SITC sector 0), which exhibits a nine-fold rise froma GL index of 0.02 in 1962 to a GL index of 0.17 in 2006. Clearly, with theincreasing sophistication and differentiation of food products, even agriculturalgoods are now subject to considerable IIT. The 1-digit sector with consistentlythe highest recorded level of 5-digit IIT, however, is ‘Machines and TransportEquipment’ (SITC sector 7).

FIGURE 6Global IIT by SITC 1-Digit Sector, 1962 and 2006

Notes: ‘wide coverage’ dataset; SITC 1-digit sectors: 0 – Food and Live Animals, 1 – Beverages and Tobacco, 2 –Crude Materials Excluding Fuels, 3 – Mineral Fuels Etc., 4 – Animal & Vegetable Oils & Fats, 5 – Chemicals,6 – Basic Manufactures, 7 – Machines & Transport Equipment, 8 – Misc. Manufactures, 9 – Goods NotClassified by Kind.



In Figure 7, I show changes in IIT separately for 3-digit sectors. While thereare now more cases of declining IIT between 1962 and 2006, it again appearsthat the rise in IIT is a pervasive phenomenon. Only 29 of the 177 3-digit sectorsexperienced a decrease in IIT over the sample period.

Figure 8 tracks the evolution of IIT separately for primary, intermediate andfinal goods. Again, it becomes apparent that the rise in IIT has been a very generalphenomenon, as it is observed for all three product groups. Primary products, notsurprisingly, have consistently exhibited the lowest IIT shares and also recordedthe slowest increase. Average IIT levels in intermediate and final goods were verysimilar until around 1975, after which IIT in intermediate goods has consistentlyexceeded IIT in final goods. This could again be taken as evidence that outwardprocessing is the dominant driver of rises in IIT over the last three decades.

c. IIT by Country and Country Group

Long-run changes in average IIT levels of individual countries are illustrated inFigures 9 and 10, for the full sample period 1962–2006, and in Figures 11 and 12,

FIGURE 7Global by SITC 3-Digit Sector, 1962 and 2006

Note: ‘wide coverage’ dataset; for sector names see Table 2.



FIGURE 8Evolution of Global IIT by Product Group, 1962–2006

Notes: Product grouping according to United Nations ‘Broad Economic Categories’; ‘long coverage’ dataset.

FIGURE 9Global IIT by Country, SITC 5-Digit, 1962 and 2006

Note: ‘wide coverage’ dataset.









for the more recent time interval 1990–2006. These plots show that IIT has beenincreasing in virtually all countries over the past 45 years. Some countries,however, have experienced declines in their IIT levels since 1990. These includeadvanced economies such as Norway, which experienced a boom in primaryexports, and Ireland, which specialised heavily in high-tech exports. Both thesecountries have experienced strong economic growth over that period, and theirexample shows that the positive association between IIT and income is notuniversal and may well be relevant only up to some critical income level.

Figures 13 to 17 document IIT patterns and trends within and between worldregions and income-based country groups. In Figure 13, I show IIT levels fortrade within 16 world regions commonly distinguished by the World Bank. IITamong industrialised economies dwarfs IIT among developing countries. While,by 2006, roughly half of internal trade in Western Europe, North America andAustralia–New Zealand was intra-industry (at the 5-digit level!), the correspond-ing shares are below 5 per cent for Western Asia and Eastern Africa and wellbelow 1 per cent for trade among Southern and Central Asian as well as amongall other African nations. The increase in IIT observed at the global level is aphenomenon that was largely confined to Europe, North America, East Asia andAustralia–New Zealand. Figure 14, which shows IIT levels for trade between aswell as within the seven broader world regions in 2006 confirms this summary





view: no trading relationship involving Africa exhibits an IIT share above 5 percent, and, with the exception of its trade with high-income countries, the same istrue for South Asia.

Detailed results on IIT and trade shares within and between the 16 worldregions for 1962 and 2006 are reported in Table 3. A striking feature of this tableis again the low IIT levels for the African regions. None of the cells of thismatrix pertaining to East Africa, Middle Africa, Northern Africa and WesternAfrica show an IIT share exceeding 5 per cent. Table 3 also shows that Africa’sshare of world trade has fallen over the sample period in a majority of thecountry combinations considered. While Africa stands out with uniquely low IITas well as trade shares, very low IIT is also observed for Western Asia (mainlyMiddle Eastern countries), whose IIT share reaches 10 per cent only for tradewith Western Europe.

Figure 15 illustrates the evolution of IIT within and between country incomegroups. Because the poorest countries are under-represented in the ‘longcoverage’ dataset (see the Appendix), I combine the World Bank’s ‘low-income’and ‘lower-middle-income’ categories into a single ‘low’ group. Again a positivecorrelation between income levels and IIT is clearly apparent, with IIT among

FIGURE 13IIT within World Regions; 1962, 1975, 1990 and 2006




FIGURE 14IIT between World Regions, 2006


FIGURE 15Evolution of Global IIT by Income Group, 1962–2006

Notes: Country grouping according to World Bank categorisation (see Table 1); ‘long coverage’ dataset.



FIGURE 16IIT within Income Groups; 1962, 1975, 1990 and 2006


FIGURE 17IIT between Income Groups; 1962, 1975, 1990 and 2006


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TABLE 3Total Trade and IIT within and among World Regions, 1962 and 2006

(‘wide coverage’ dataset)Organisation of cells:1st row: % share in world trade, 19622nd row: % share in world trade, 20063rd row: GL index, 5-digit, 1962

4th row: GL index, 5-digit, 2006

AUS CAC CACT EAF EEUR MAF NAF NAM NEAS SAF SAM SAS SEAP WAF WAS WEUR

AUS n.a.0.0882n.a.

0.448

CAC 0.0009 0.00600.0117 0.07530.000 0.029

0.128 0.118

CACT 0.0003 0.0000 n.a.0.0024 0.0013 0.02910.000 0.000 n.a.

0.008 0.037 0.012

EAF 0.0000 0.0000 0.0000 0.00130.0013 0.0000 0.0012 0.00660.000 0.000 0.000 0.000

0.005 0.004 0.002 0.027

EEUR 0.0003 0.0001 0.0062 0.0002 0.00640.0078 0.0131 0.3610 0.0011 1.37650.000 0.000 0.000 0.000 0.000

0.047 0.119 0.080 0.006 0.204

MAF 0.0000 0.0001 0.0000 0.0012 0.0000 0.00150.0000 0.0000 0.0001 0.0006 0.0003 0.00050.000 0.000 0.000 0.000 0.000 0.007

0.001 0.001 0.000 0.000 0.001 0.022

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NAF 0.0000 0.0014 0.0001 0.0008 0.0049 0.0010 0.00820.0018 0.0010 0.0147 0.0043 0.0178 0.0002 0.00740.000 0.000 0.000 0.000 0.000 0.003 0.001

0.007 0.016 0.018 0.004 0.005 0.000 0.003

NAM 0.4776 3.0835 0.2414 0.0416 0.1514 0.0831 0.2016 5.93910.3579 4.0709 0.1125 0.0115 0.4171 0.0753 0.1699 5.02390.000 0.034 0.001 0.000 0.008 0.000 0.001 0.107

0.194 0.381 0.073 0.017 0.142 0.001 0.004 0.553

NEAS 0.5474 0.1630 0.0080 0.0222 0.0042 0.0028 0.0128 5.5298 0.72031.0750 0.5709 0.1764 0.0685 0.8971 0.0977 0.0716 8.5216 9.02460.000 0.001 0.001 0.000 0.000 0.000 0.000 0.044 0.010

0.042 0.110 0.022 0.003 0.053 0.000 0.014 0.208 0.270

SAF n.a. 0.0001 0.0000 0.0000 0.0000 0.0007 0.0002 0.2785 0.1106 n.a.0.0246 0.0028 0.0033 0.0539 0.0087 0.0107 0.0016 0.1399 0.2234 0.0550n.a. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 n.a.

0.142 0.092 0.021 0.021 0.054 0.000 0.011 0.149 0.092 0.002

SAM 0.0004 0.1013 0.0000 0.0000 0.0074 0.0000 0.0002 5.6383 0.3211 0.0010 0.16320.0121 0.2032 0.0058 0.0006 0.0464 0.0106 0.0152 1.4266 0.7171 0.0170 0.53440.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.001 0.000 0.002

0.050 0.119 0.033 0.002 0.028 0.000 0.001 0.101 0.024 0.062 0.202


Table 3 Continued

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SAS 0.0148 0.0002 0.0002 0.0047 0.0066 0.0000 0.0028 0.8909 0.2838 0.0018 0.0017 0.01980.0556 0.0165 0.0143 0.0168 0.0530 0.0022 0.0180 0.4649 0.5541 0.0255 0.0405 0.03640.000 0.000 0.001 0.000 0.000 0.000 0.000 0.006 0.001 0.000 0.000 0.010

0.049 0.054 0.070 0.005 0.056 0.000 0.010 0.153 0.119 0.083 0.045 0.006

SEAP 0.0388 0.0004 0.0000 0.0003 0.0020 0.0000 0.0002 1.0881 1.1692 0.0037 0.0011 0.0358 0.39880.3244 0.0599 0.0113 0.0052 0.0578 0.0014 0.0083 1.8110 4.3765 0.0282 0.0525 0.2454 1.19040.000 0.000 0.000 0.000 0.000 0.000 0.000 0.008 0.005 0.000 0.000 0.020 0.000

0.114 0.128 0.029 0.010 0.059 0.001 0.026 0.251 0.305 0.046 0.038 0.134 0.357

WAF 0.0003 0.0001 0.0000 0.0001 0.0015 0.0010 0.0045 0.1501 0.0375 0.0001 0.0003 0.0012 0.0001 0.01060.0006 0.0002 0.0005 0.0000 0.0019 0.0009 0.0008 0.1876 0.0480 0.0150 0.0140 0.0572 0.0043 0.00960.000 0.000 0.000 0.000 0.000 0.005 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.014

0.001 0.002 0.000 0.003 0.000 0.001 0.003 0.000 0.000 0.001 0.000 0.000 0.001 0.003WAS 0.0002 0.0001 0.0083 0.0003 0.0050 0.0001 0.0000 0.5702 0.2610 0.0018 0.0008 0.0098 0.0047 0.0013 0.0000

0.0342 0.0047 0.0530 0.0224 0.0450 0.0001 0.0352 0.8809 1.5932 0.0387 0.0338 0.4296 0.2754 0.0012 0.15360.000 0.001 0.003 0.000 0.003 0.000 0.000 0.066 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.010 0.024 0.024 0.005 0.035 0.000 0.037 0.087 0.016 0.009 0.007 0.030 0.017 0.000 0.033

WEUR 1.0555 0.8341 0.4949 0.4778 1.0902 0.6354 2.9560 13.7417 1.3188 0.6390 4.0897 0.8955 0.8431 1.3374 1.7037 38.95700.4075 0.4139 0.8525 0.0788 5.9979 0.0777 0.7301 6.2707 6.1474 0.4119 0.9011 0.6332 1.2810 0.1629 0.9705 24.97030.000 0.004 0.012 0.006 0.012 0.002 0.008 0.088 0.047 0.000 0.003 0.013 0.003 0.004 0.004 0.190

0.112 0.157 0.182 0.032 0.308 0.003 0.049 0.405 0.229 0.126 0.097 0.201 0.208 0.006 0.103 0.457

Abbreviations (World Bank geographic regions)AUS: Australia & New Zealand; CAC: Central America & Caribbean; CACT: Central Asia, Caucasus & Turkey; EAF: Eastern Africa; EEUR: Eastern Europe &Russia; MAF: Middle Africa; NAF: Northern Africa; NAM: North America; NEAS: Northeast Asia; SAF: Southern Africa; SAM: South America; SAS: SouthernAsia; SEAP: Southeast Asia & Pacific; WAF: Western Africa; WAS: Western Asia; WEUR: Western Europe.




high-income countries far outstripping IIT among all other country groups. Therehas, however, been some marked convergence in global IIT patterns, with IITshares among all country groups trending upwards since around 1980, and IITshares involving middle-income and low-income countries growing more rapidlythan IIT among high-income countries.

One conspicuous pattern in Figure 15 is a levelling-off in all IIT series,coinciding roughly with the turn of the millennium. A similar, though lesspronounced, trend break is also visible in the aggregate IIT time paths shown inFigure 3. Figure 15 shows that the recent stagnation in aggregate IIT growth isnot due to the increased integration into world trade of emerging economies andan associated inter-industry ‘re-specialisation’, because all country groups exhibitslowdowns.20 One possibility is that IIT has levelled off because of the recentincrease in the share of primary goods in the value of world trade. Only some6 per cent of global trade in primary goods were intra-industry in 2006 (seeFigure 2).

Being based on the ‘long coverage’ sample, Figure 15 offers a continuous timeseries, but it does not take account of most of the world’s poorest countries.Figures 16 and 17, being based on the ‘wide coverage’ dataset, address this issue.The exclusion from global IIT of the poorest countries emerges starkly fromFigure 16. Among countries categorised as ‘low-income’ by the World Bank, theintra-group IIT share has remained stuck below a derisory 0.5 per cent since1962. The convergence in global IIT levels is clearly a middle-income countryphenomenon. The surge in IIT among the lower-middle-income countries from2.2 per cent in 1990 to 13.9 per cent in 2006 is particularly striking.

The polarised global geography of IIT is also apparent in Figure 17, where Ireport the evolution of IIT between income groups: everybody’s average IIT ishighest with the high-income countries and lowest with the low-income countries.

d. IIT and Regional Integration

In light of the ongoing proliferation of regional integration agreements (RIAs),I report some relevant evidence for the EU and for four Sub-Saharan AfricanRIAs.

Figures 18 and 19 show the evolution of IIT and of intra-RIA trade sharesfor the EU-15 and for the EU-27, respectively. The internal trade share hasbeen increasing steadily since the early 1960s, and intra-EU IIT has risen inparallel. Thus, European integration has gone hand-in-hand with significantstrengthening of intra-European trading relations as well as with increasingstructural similarity of the participating economies. The coexistence of trade

20 Note, furthermore, that China does not feature in the ‘long coverage’ dataset (see the Appendix).Its economic ascent cannot therefore explain the observed patterns.



FIGURE 18IIT of the EU-15; 1962, 1975, 1990 and 2006

Notes: ‘wide coverage’ dataset; EU-15 (since 1995): Austria, Belgium, Denmark, Finland, France, Germany, Greece,Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom.

FIGURE 19IIT of the EU-27; 1962, 1975, 1990 and 2006

Notes: ‘wide coverage’ dataset; EU-27 (since 2007): EU-15 + Bulgaria, Cyprus, Czech Republic, Estonia, Hungary,Latvia, Lithuania, Malta, Poland, Romania, Slovakia, Slovenia.



expansion and increasing sectoral similarity across member states that surprisedresearchers in the early years of European integration (e.g. Balassa, 1966) thuscontinued to mark the evolution of the European economy over the subsequentfour decades.

Figures 20 to 23 show comparable statistics for four African RIAs. Theseintegration schemes differ substantially in terms of age and institutional depth, butmy calculations show that they resemble each other in two fundamental respects.First, both intra-RIA trade shares and average levels of IIT are extremely low inthose RIAs compared to the EU. In Africa, intra-RIA IIT in no case exceeds 2per cent, whereas in the EU-15 it reached 46 per cent in 2006. Second, in Africaneither intra-RIA trade shares nor intra-RIA IIT show any clear time trends. Onthe basis of these (rather rough) computations, there is evidence of African RIAshaving stimulated neither substantial regional trade nor structural convergence.

5. SOME SIMPLE REGRESSIONS: IIT, INCOME AND DISTANCE OVER FOUR DECADES

As a complement to the descriptive statistics that represent the main con-tribution of this paper, I report some simple regression results to quantify the

FIGURE 20IIT of the Central African Economic and Monetary Community (CEMAC),

1962, 1975, 1990 and 2006

Notes: ‘wide coverage’ dataset; CEMAC (since 1999): Cameroon, Central African Republic, Chad, Republic of Congo,Equatorial Guinea, Gabon.



FIGURE 21IIT of the West African Economic and Monetary Union (WAEMU); 1962, 1975, 1990 and 2006

Notes: ‘wide coverage’ dataset; WAEMU (since 1997): Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali,Niger, Senegal, Togo.

FIGURE 22IIT of the East African Community (EAC); 1962, 1975, 1990 and 2006

Notes: ‘wide coverage’ dataset; EAC (since 2007): Burundi, Kenya, Rwanda, Tanzania, Uganda.



sensitivity of IIT to bilateral distance as well as its relation to per-capita incomelevels. The main value added here is that I can trace how these parameters haveevolved over time, and that I run the regression separately for primary, inter-mediate and final goods sectors.

I estimate the following regression equation separately by sample year:

(7)

where GLcd is the aggregate bilateral GL index between countries c and d asdefined in (2), pcGDP is per-capita GDP, dist is the geodesic distance betweenthe two countries’ main cities, and contig is a dummy variable set to one forcountries that share a common land border. The dependent variable is a logtransformation of the GL index, which centres it symmetrically around zero andmakes it unbounded.21 Specification (7) contains the main variables featuring in

21 In order not to lose bilateral observations with no IIT, I have set GLcd = 0.0001 for all countrypairs with zero recorded IIT, this number being slightly lower than the smallest observed non-zerobilateral GL index. The qualitative results are fairly robust to the particular choice of this number.

FIGURE 23IIT of the Southern African Customs Union (SACU); 1962, 1975, 1990 and 2006

Notes: ‘wide coverage’ dataset; SACU (since 1990): Botswana, Lesotho, Namibia, South Africa, Swaziland.

ln

ln

ln

ln( ) ln( ) ,

GLGL

pcGDP pcGDPpcGDP

pcGDP dist contig

cd

cd

c dc

d cd cd cd

1 20 1 2

3 4

−⎛⎝⎜

⎞⎠⎟

= ++⎛

⎝⎜⎞⎠⎟

+

− + + +

β β β

β β ε

|

|



most cross-country IIT regressions: the joint income level of the country pair,which is commonly associated with high IIT; the difference in income levels,which is associated with low IIT; and distance measures, which are also associatedwith low IIT.22

Table 5 reports full regression results for three sample years, 1965, 1990 and2006. The model explains between 27 and 41 per cent of the sample variance inbilateral IIT, and the findings of numerous previous papers (as well as of theprevious two sections of this paper) are confirmed: high-income and proximatecountry pairs have higher IIT than low-income and/or distant country pairs. Thisapplies across all three types of goods. Only the difference in per-capita GDPdoes not seem to affect bilateral IIT shares systematically: while there areinstances of statistically significant positive as well as negative coefficients, thelarge majority of estimates are statistically not significantly different from zero.

The main output from this exercise is Figure 24, which traces the annualestimated coefficients on distance and on average GDP per capita over the sampleperiod. Two tendencies are apparent. First, the estimated coefficients on per-capita incomes were generally increasing until around 1982 but have been fallingsteadily since. This implies that, while IIT continues to be largely confined tohigh-income countries, this link has been weakening somewhat over the lastquarter of a century. In 1982, the estimated elasticity of bilateral IIT with respectto average per-capita GDP (b1) stood at 2.47, whereas by 2006 it had fallento 1.62. IIT thus seems to be increasingly characterising trade involvingmiddle-income and low-income countries as well.

The coefficients on distance, shown in the lower part of Figure 24, havegradually shrunk in absolute magnitude. While the elasticity of IIT with respectto distance stood at −1.46 in 1965, it had reached a value of −0.70 – still highlystatistically significant, but only half as large as some 40 years earlier. Thereduction in the distance sensitivity of aggregate bilateral IIT has been drivenmainly by IIT in intermediate goods. This could be taken as another piece ofindicative evidence for the growing weight of intermediate (outward processing)trade in global IIT, and it suggests that two-way intermediates trade on averagestretches over larger distances than two-way trade in primary and final goods.

6. MARGINAL IIT

Figures 25 to 29 illustrate the broad patterns of global MIIT, computed usingdefinitions (5) and (6), and Table 4 lists MIIT indices by country. All trade valuesunderlying the reported indices are converted into constant prices using the USGDP deflator.

22 See e.g. Hummels and Levinsohn (1995) and Bergstrand and Egger (2006).

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TABLE 4Cross-Country Determinants of IIT, 1965, 1990 and 2006

(Dependent variable = log transformed GL index, estimation by OLS)

1965 1990 2006

All Sectors Primary Intermed. Final All Sectors Primary Intermed. Final All Sectors Primary Intermed. Final

log mean per-cap. GDP

1.753*** 1.322*** 1.944*** 1.854*** 2.193*** 1.855*** 2.378*** 2.045*** 1.617*** 1.534*** 1.918*** 1.513***(0.09) (0.11) (0.11) (0.12) (0.09) (0.10) (0.10) (0.10) (0.08) (0.10) (0.08) (0.08)

log diff per-cap. GDP

−0.0811 0.018 −0.133 −0.210** 0.0890 0.00854 0.140* −0.132 0.0444 −0.097 0.189*** −0.0668(0.08) (0.09) (0.09) (0.09) (0.08) (0.08) (0.08) (0.09) (0.07) (0.09) (0.07) (0.07)

log distance −1.464*** −1.092*** −1.231*** −1.754*** −1.163*** −1.019*** −1.021*** −1.285*** −0.700*** −1.161*** −0.622*** −0.923***(0.10) (0.11) (0.11) (0.11) (0.10) (0.10) (0.11) (0.11) (0.09) (0.11) (0.09) (0.09)

contiguity 1.330*** 1.827*** 1.464*** 0.890* 1.486*** 1.801*** 1.812*** 0.969* 1.571*** 1.672*** 2.006*** 1.327***(0.47) (0.50) (0.51) (0.53) (0.48) (0.50) (0.51) (0.52) (0.41) (0.53) (0.45) (0.44)

constant −9.555*** −10.500*** −13.500*** −7.902*** −14.730*** −15.180*** −17.591*** −12.263*** −12.570*** −10.361*** −16.150*** −9.665***(1.23) (1.35) (1.35) (1.43) (1.26) (1.34) (1.36) (1.40) (1.12) (1.44) (1.21) (1.20)

Observations 1,196 1,090 1,101 1,069 1,411 1,340 1,373 1,354 1,375 1,354 1,374 1,373R2 0.41 0.27 0.37 0.39 0.41 0.32 0.39 0.36 0.33 0.28 0.34 0.31

***, ** and * indicate statistical significance at the 1 per cent, 5 per cent and 10 per cent levels, respectively. Numbers in parentheses are standard errors.

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FIGURE 24Sensitivity of IIT to Distance and Income, 1965–2006

Note: Coefficients from annual cross-section regressions analogous to those reported in Table 4.



FIGURE 25Global MIIT over Five Decades

Notes: ‘long coverage’ dataset; data converted into constant prices using US GDP deflator; base and end periods areaverages of three adjacent years.

FIGURE 26MIIT by Income Group

Notes: Country grouping according to World Bank categorisation (see Table 1, ‘Low’ category is combination of LICand LMC); ‘long coverage’ dataset; data converted into constant prices using US GDP deflator; base and endperiods are averages of three adjacent years.



First, I report aggregate MIIT indices for each of my five sample decades (the‘1960s’ starting in 1962 and the ‘2000s’ ending in 2006), taking three adjacentyears for the base and end periods in order to smooth out any year-specificvariations. What emerges in Figure 25 is a remarkably stable level of MIIT. Onaverage, about one-fifth of trade expansion was in the form of bilaterally matchedimport and export changes at the 5-digit level. Hence, the bulk of trade changesinvolve inter-industry adjustments. The observed secular increase in IIT thereforewas not accompanied by an equivalent rise in MIIT. While static IIT has beenincreasing strongly, the pressures for intersectoral factor reallocations implied bythis trade expansion do not appear to have lessened proportionally over time.23

23 It does, however, appear that MIIT was considerably higher in the 1990s than in the threeprevious decades, and the apparent drop in MIIT in the 2000s could be due to the shorter timeinterval considered. This may therefore suggest that MIIT is on the rise too, but with a certain lagcompared to the increases in IIT.

FIGURE 27MIIT by Income Group, Primary Goods

Notes: Country grouping according to World Bank categorisation (see Table 1, ‘Low’ category = LIC + LMC); productgrouping according to United Nations ‘Broad Economic Categories’; ‘long coverage’ dataset; data convertedinto constant prices using US GDP deflator; base and end periods are averages of three adjacent years.



In Figures 26 to 29, decade-by-decade MIIT is shown separately for countrygroups by income level and for sector groups by processing stage. Two patternsemerge very clearly: averaged across product groups, MIIT is highest among thehigh-income countries; and averaged across countries, MIIT is highest in theintermediate goods category. Adjustment to trade expansion is thus likely tobe smoother for trade among high-income countries and in intermediate goodsectors.

Of all the cases distinguished in Figures 26 to 29, the highest level of MIIT(0.37) is observed for trade between middle-income and high-income countriesin intermediate goods in the 1990s. Once again, this evidence suggests thatoutward processing trade is the main driving force towards higher observedincreases in IIT and MIIT in recent years.

Table 5 shows MIIT measures country-by-country for three long periods ofsome 15 years each, using the ‘wide coverage’ sample. Countries are sorted indecreasing order of their share in average gross changes in global trade volumes

FIGURE 28MIIT by Income Group, Intermediate Goods




over the total 1962–2006 interval. I find that the large industrialised countriesagain feature at the top of the list. The most sectorally balanced trade expansionover the full interval is recorded for Austria (MIIT index of 0.45). In the 1990–2006 sub-period the highest value is obtained for Hungary (MIIT index of 0.51),followed by Austria (0.49) and Canada (0.45). For most countries, however,MIIT is tiny. Over the 1990–2006 sub-period, 141 of the 190 sample countrieshave an MIIT index below 0.1, suggesting that more than 90 per cent of theirtrade changes (generally in the form of expansion) implied reallocations betweenrather than within 5-digit industries.24 For most countries, therefore, trade expansioncontinues to entail primarily inter-industry adjustments.

24 I can compute MIIT for only 190 of the 214 countries in the ‘wide coverage’ dataset, becauseI need to observe trade for both the base and the end year.

FIGURE 29MIIT by Income Group, Final Goods


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TABLE 5MIIT by Country, 1962–75, 1975–90 and 1990–2006


Country MIIT 1962–75

MIIT 1975–90

MIIT1990–2006

MIIT Average

% of Total Tr. Change, 1962–75



% of Total Tr. Change, Average

United States 0.226 0.353 0.343 0.307 21.974 19.327 21.282 20.861Germany 0.335 0.484 0.437 0.419 18.329 15.838 11.428 15.198France 0.227 0.481 0.420 0.376 9.624 9.887 5.413 8.308Japan 0.103 0.230 0.270 0.201 8.749 9.306 6.213 8.089United Kingdom 0.326 0.435 0.337 0.366 7.921 8.986 5.301 7.403Italy 0.239 0.399 0.361 0.333 7.267 6.629 4.030 5.975Netherlands 0.345 0.439 0.281 0.355 6.895 4.592 3.154 4.880China 0.028 0.227 0.252 0.169 0.088 1.017 11.326 4.143Belgium-Luxembourg 0.411 0.503 n.a. 0.457 4.401 3.597 n.a. 3.999Canada 0.356 0.453 0.445 0.418 2.531 2.475 3.167 2.724Spain 0.190 0.400 0.398 0.329 0.598 1.925 2.599 1.707Sweden 0.322 0.377 0.340 0.346 2.130 1.249 0.869 1.416Switzerland 0.332 0.458 0.409 0.400 1.106 1.859 1.131 1.365Hong Kong, China 0.141 0.293 0.130 0.188 0.488 1.580 1.921 1.330Korea, Rep. 0.200 0.254 0.307 0.253 0.069 0.789 2.749 1.202Taiwan, China 0.050 0.262 0.321 0.211 0.079 0.887 2.304 1.090Singapore 0.134 0.320 0.335 0.263 0.345 0.931 1.525 0.933Denmark 0.299 0.347 0.321 0.322 1.115 0.770 0.599 0.828Malaysia n.a. 0.278 0.343 0.310 n.a. 0.396 1.108 0.752Australia 0.060 0.125 0.116 0.100 0.358 0.800 1.094 0.751Mexico 0.171 0.311 0.391 0.291 0.236 0.357 1.596 0.730Austria 0.364 0.487 0.488 0.447 0.332 0.978 0.812 0.707Brazil 0.071 0.120 0.205 0.132 0.489 0.411 0.862 0.587India 0.029 0.117 0.183 0.110 0.198 0.280 1.107 0.528Norway 0.283 0.195 0.139 0.205 0.597 0.440 0.475 0.504Thailand 0.041 0.221 0.328 0.197 0.103 0.342 1.030 0.492Saudi Arabia 0.003 0.021 0.017 0.014 0.261 0.373 0.666 0.434Ireland 0.377 0.393 0.265 0.345 0.070 0.268 0.570 0.303

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Indonesia 0.011 0.057 0.148 0.072 0.137 0.235 0.456 0.276Finland 0.207 0.313 0.247 0.256 0.121 0.329 0.329 0.260Portugal 0.178 0.314 0.308 0.267 0.107 0.280 0.299 0.228Turkey 0.029 0.110 0.191 0.110 0.105 0.148 0.401 0.218Unspecified 0.005 0.007 0.015 0.009 0.113 0.330 0.198 0.214Greece 0.094 0.133 0.149 0.126 0.214 0.201 0.205 0.207Venezuela 0.008 0.055 0.042 0.035 0.285 0.103 0.206 0.198Yugoslavia, FR 0.139 0.239 n.a. 0.189 0.182 0.190 n.a. 0.186Poland 0.086 0.086 0.435 0.202 0.084 0.061 0.408 0.184Philippines 0.036 0.144 0.372 0.184 0.098 0.088 0.299 0.162Israel 0.118 0.321 0.284 0.241 0.153 0.133 0.183 0.156South Africa 0.052 0.032 0.207 0.097 0.173 0.091 0.198 0.154Argentina 0.063 0.091 0.214 0.123 0.162 0.091 0.198 0.150Iran, Islamic Rep. 0.008 0.006 0.022 0.012 0.277 0.077 0.072 0.142Chile 0.025 0.051 0.049 0.041 0.052 0.057 0.224 0.111Soviet Union 0.036 0.028 n.a. 0.032 0.159 0.168 0.000 0.109New Zealand 0.034 0.145 0.192 0.124 0.038 0.115 0.123 0.092Hungary 0.157 0.058 0.506 0.241 0.022 0.025 0.215 0.087United Arab Emirates n.a. 0.010 0.013 0.012 0.000 0.052 0.206 0.086Nigeria 0.006 0.003 0.003 0.004 0.117 0.043 0.057 0.073Algeria 0.012 0.009 0.007 0.009 0.078 0.057 0.076 0.070Pakistan 0.050 0.033 0.043 0.042 0.034 0.051 0.096 0.060Kuwait 0.004 0.008 0.005 0.006 0.057 0.048 0.071 0.058Morocco 0.018 0.079 0.135 0.077 0.077 0.040 0.055 0.058Colombia 0.042 0.066 0.130 0.080 0.033 0.048 0.089 0.057Libya 0.006 0.005 0.004 0.005 0.108 0.033 0.028 0.056Egypt, Arab Rep. 0.017 0.039 0.056 0.038 0.032 0.061 0.070 0.054Romania 0.095 0.060 0.298 0.151 0.018 0.023 0.118 0.053Peru 0.012 0.044 0.049 0.035 0.061 0.022 0.057 0.047Czechoslovakia 0.108 0.112 n.a. 0.110 0.041 0.047 n.a. 0.044Iraq 0.003 0.003 0.003 0.003 0.054 0.035 0.022 0.037Qatar 0.008 0.011 0.011 0.010 0.003 0.009 0.081 0.031Tunisia 0.054 0.148 0.026 0.076 0.022 0.025 0.022 0.023Oman 0.029 0.032 0.014 0.025 0.001 0.009 0.054 0.022Ecuador 0.016 0.021 0.082 0.040 0.019 0.013 0.032 0.021


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Costa Rica 0.027 0.076 0.175 0.093 0.006 0.012 0.042 0.020Panama 0.023 0.036 0.076 0.045 0.021 0.018 0.019 0.019Syrian Arab Republic 0.022 0.007 0.031 0.020 0.010 0.023 0.019 0.017Trinidad and Tobago 0.024 0.039 0.024 0.029 0.016 0.009 0.024 0.017German Democratic

Republic0.103 0.047 n.a. 0.075 0.016 0.017 n.a. 0.017

Netherlands Antilles 0.012 0.014 0.027 0.018 0.029 0.011 0.005 0.015Sri Lanka 0.008 0.061 0.028 0.032 0.012 0.015 0.018 0.015Jordan 0.013 0.050 0.041 0.035 0.003 0.011 0.030 0.015Bulgaria 0.106 0.066 0.255 0.142 0.006 0.005 0.031 0.014Côte d’Ivoire 0.015 0.007 0.012 0.012 0.027 0.008 0.006 0.014Cyprus 0.057 0.071 0.136 0.088 0.003 0.012 0.023 0.013Guatemala 0.015 0.065 0.094 0.058 0.007 0.009 0.021 0.013Jamaica 0.086 0.115 0.077 0.093 0.015 0.009 0.008 0.011Bahrain 0.032 0.029 0.055 0.039 0.005 0.012 0.015 0.011Bangladesh n.a. 0.026 0.014 0.020 0.000 0.007 0.024 0.010Ghana 0.034 0.013 0.027 0.025 0.021 0.004 0.005 0.010Lebanon 0.016 0.012 0.028 0.019 0.015 0.008 0.006 0.010Iceland 0.032 0.035 0.065 0.044 0.008 0.010 0.011 0.010Angola 0.008 0.002 0.001 0.004 0.006 0.007 0.015 0.009Cameroon 0.012 0.026 0.007 0.015 0.011 0.007 0.007 0.009Bolivia 0.010 0.009 0.034 0.018 0.015 0.005 0.005 0.008Uruguay 0.043 0.108 0.113 0.088 0.007 0.008 0.010 0.008Dominican Republic 0.045 0.012 0.031 0.030 0.009 0.008 0.008 0.008Malta 0.115 0.405 0.300 0.273 0.004 0.009 0.011 0.008Kenya 0.031 0.027 0.016 0.025 0.007 0.007 0.008 0.007Congo, Dem. Rep. 0.014 0.009 0.005 0.009 0.016 0.005 0.001 0.007Liberia 0.018 0.027 0.015 0.020 0.009 0.007 0.004 0.007Senegal 0.019 0.059 0.047 0.042 0.010 0.006 0.004 0.007


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Vietnam n.a. 0.024 0.049 0.036 0.000 0.001 0.018 0.006El Salvador 0.023 0.067 0.095 0.062 0.005 0.004 0.010 0.006Honduras 0.030 0.038 0.067 0.045 0.003 0.004 0.011 0.006Paraguay 0.028 0.013 0.039 0.027 0.004 0.005 0.008 0.006Brunei 0.016 0.013 0.008 0.012 0.002 0.007 0.007 0.005Mauritius 0.014 0.067 0.070 0.051 0.001 0.008 0.006 0.005Cuba 0.009 0.009 0.012 0.010 0.007 0.004 0.003 0.005Sudan 0.012 0.012 0.012 0.012 0.007 0.002 0.005 0.005Special Categories 0.130 0.076 0.040 0.082 0.006 0.005 0.001 0.004Gabon 0.006 0.003 0.004 0.005 0.007 0.003 0.002 0.004Macao 0.000 0.136 0.099 0.078 0.000 0.004 0.008 0.004Nicaragua 0.015 0.041 0.043 0.033 0.004 0.002 0.004 0.004Madagascar 0.014 0.030 0.021 0.022 0.005 0.002 0.003 0.003Myanmar 0.008 0.010 0.012 0.010 0.005 0.001 0.002 0.003Bahamas, The 0.043 0.015 0.019 0.026 0.003 0.002 0.003 0.003Papua New Guinea 0.007 0.015 0.006 0.009 0.001 0.003 0.003 0.002Mozambique 0.010 0.009 0.005 0.008 0.003 0.001 0.003 0.002Ethiopia (includes Eritrea) 0.011 0.020 n.a. 0.016 0.003 0.003 0.000 0.002Congo, Rep. 0.007 0.004 0.002 0.004 0.002 0.002 0.002 0.002Zambia n.a. 0.007 0.003 0.005 0.000 0.003 0.003 0.002Guadeloupe 0.053 0.027 n.a. 0.040 0.001 0.003 n.a. 0.002New Caledonia 0.002 0.004 0.021 0.009 0.003 0.001 0.001 0.002Togo 0.009 0.012 0.004 0.008 0.003 0.001 0.001 0.002Fiji 0.006 0.044 0.057 0.036 0.000 0.002 0.002 0.002Bunkers 0.020 0.001 0.000 0.007 0.003 0.001 0.000 0.002Martinique 0.066 0.030 n.a. 0.048 0.000 0.003 n.a. 0.002Tanzania n.a. 0.013 0.011 0.012 0.000 0.002 0.003 0.002Barbados 0.104 0.095 0.050 0.083 0.001 0.002 0.002 0.001Réunion 0.024 0.017 n.a. 0.020 0.001 0.003 0.000 0.001Zimbabwe n.a. 0.056 0.017 0.036 0.000 0.000 0.004 0.001Guyana 0.026 0.025 0.022 0.025 0.002 0.001 0.000 0.001Afghanistan 0.012 0.011 0.009 0.011 0.002 0.000 0.000 0.001Korea, Dem. Rep. 0.003 0.027 0.028 0.019 0.000 0.001 0.002 0.001Suriname 0.110 0.016 0.026 0.051 0.002 0.001 0.000 0.001Mali 0.049 0.018 0.008 0.025 0.001 0.001 0.001 0.001


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Bermuda 0.018 0.022 0.004 0.015 0.000 0.001 0.002 0.001Haiti 0.055 0.034 0.040 0.043 0.001 0.001 0.001 0.001Malawi n.a. 0.006 0.020 0.013 0.000 0.002 0.001 0.001Benin 0.002 0.004 0.001 0.002 0.001 0.000 0.001 0.001Nepal 0.019 0.028 0.036 0.027 0.000 0.001 0.001 0.001Burkina Faso 0.005 0.004 0.003 0.004 0.001 0.000 0.000 0.001Sierra Leone 0.007 0.005 0.016 0.009 0.001 0.000 0.000 0.001Guinea 0.001 0.006 0.005 0.004 0.000 0.001 0.001 0.001Uganda 0.003 0.004 0.009 0.005 0.000 0.000 0.001 0.001French Polynesia 0.015 0.014 0.020 0.016 0.000 0.001 0.001 0.000Albania 0.027 0.031 0.249 0.102 0.000 0.000 0.001 0.000Mauritania 0.000 0.002 0.009 0.004 0.000 0.000 0.000 0.000Yemen Democratic 0.003 0.014 n.a. 0.009 0.001 0.001 0.000 0.000Faeroe Islands n.a. 0.040 0.053 0.046 0.000 0.000 0.001 0.000Niger 0.012 0.005 0.009 0.008 0.001 0.000 0.000 0.000St. Lucia n.a. 0.090 0.058 0.074 0.000 0.001 0.000 0.000Seychelles n.a. 0.021 0.088 0.054 0.000 0.000 0.001 0.000Yemen 0.001 0.004 0.013 0.006 0.000 0.000 0.001 0.000Andorra n.a. 0.041 0.040 0.041 n.a. 0.000 0.000 0.000Central African Republic 0.004 0.003 0.008 0.005 0.001 0.000 0.000 0.000Chad 0.010 0.003 0.007 0.007 0.001 0.000 0.000 0.000Cambodia 0.007 0.001 0.002 0.004 0.000 0.000 0.001 0.000Somalia 0.005 0.004 0.010 0.006 0.001 0.000 0.000 0.000French Guiana 0.001 0.025 n.a. 0.013 0.000 0.001 0.000 0.000Belize 0.000 0.048 0.028 0.025 0.000 0.000 0.000 0.000Djibouti 0.004 0.015 0.006 0.008 0.000 0.000 0.000 0.000Mongolia 0.000 0.021 0.014 0.012 0.000 0.000 0.001 0.000Greenland n.a. 0.014 0.012 0.013 0.000 0.000 0.000 0.000Cayman Islands n.a. 0.009 0.004 0.006 0.000 0.000 0.000 0.000Lao PDR 0.001 0.039 0.010 0.017 0.000 0.000 0.000 0.000


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Gibraltar 0.001 0.040 0.009 0.017 0.000 0.000 0.000 0.000Aruba n.a. n.a. 0.008 0.008 0.000 0.000 0.000 0.000Us Msc. Pac. I 0.005 0.009 n.a. 0.007 0.000 0.000 0.000 0.000Gambia, The 0.002 0.008 0.006 0.005 0.000 0.000 0.000 0.000Maldives n.a. 0.029 0.008 0.019 0.000 0.000 0.000 0.000Burundi 0.004 0.010 0.010 0.008 0.000 0.000 0.000 0.000Antigua and Barbuda n.a. 0.015 0.003 0.009 0.000 0.000 0.000 0.000Vanuatu 0.001 0.004 0.010 0.005 0.000 0.000 0.000 0.000Cape Verde 0.000 0.008 0.030 0.013 0.000 0.000 0.000 0.000St. Vincent and

the Grenadinesn.a. 0.037 0.015 0.026 0.000 0.000 0.000 0.000

Rwanda n.a. 0.004 0.004 0.004 0.000 0.000 0.000 0.000Equatorial Guinea 0.001 0.010 0.007 0.006 0.000 0.000 0.000 0.000Samoa 0.005 0.037 0.027 0.023 0.000 0.000 0.000 0.000Free Zones 0.001 0.000 0.001 0.001 0.000 0.000 0.000 0.000Fm Panama Cz 0.002 n.a. n.a. 0.002 0.000 0.000 0.000 0.000Guinea-Bissau 0.009 0.016 0.008 0.011 0.000 0.000 0.000 0.000Dominica n.a. 0.017 0.035 0.026 0.000 0.000 0.000 0.000Solomon Islands n.a. 0.010 0.006 0.008 0.000 0.000 0.000 0.000Grenada n.a. 0.046 0.021 0.033 0.000 0.000 0.000 0.000St. Kitts and Nevis n.a. n.a. 0.155 0.155 0.000 0.000 0.000 0.000British Virgin Islands n.a. 0.026 0.028 0.027 0.000 0.000 0.000 0.000Tonga n.a. 0.020 0.010 0.015 0.000 0.000 0.000 0.000Comoros 0.000 0.004 0.002 0.002 0.000 0.000 0.000 0.000Saint Pierre and Miquelon 0.000 0.008 0.003 0.004 0.000 0.000 0.000 0.000Montserrat n.a. 0.006 0.025 0.015 0.000 0.000 0.000 0.000Turks and Caicos Isl. n.a. 0.108 0.003 0.055 0.000 0.000 0.000 0.000Kiribati n.a. 0.004 0.012 0.008 0.000 0.000 0.000 0.000Bhutan n.a. 0.008 0.022 0.015 0.000 0.000 0.000 0.000Falkland Islands n.a. 0.157 0.012 0.084 0.000 0.000 0.000 0.000São Tomé and Principe n.a. 0.009 0.025 0.017 0.000 0.000 0.000 0.000Cook Islands n.a. 0.002 0.011 0.007 0.000 0.000 0.000 0.000Nauru n.a. 0.000 0.000 0.000 0.000 0.000 0.000 0.000East Timor 0.003 0.000 0.000 0.001 0.000 0.000 0.000 0.000Anguila n.a. n.a. 0.007 0.007 0.000 0.000 0.000 0.000


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Saint Helena n.a. 0.031 0.005 0.018 0.000 0.000 0.000 0.000Wallis and Futura Isl. n.a. 0.000 0.004 0.002 0.000 0.000 0.000 0.000Christmas Island n.a. 0.000 0.005 0.003 0.000 0.000 0.000 0.000Norfolk Island n.a. 0.007 0.008 0.007 0.000 0.000 0.000 0.000Cocos (Keeling) Islands n.a. 0.018 0.046 0.032 0.000 0.000 0.000 0.000Tuvalu n.a. n.a. 0.007 0.007 0.000 0.000 0.000 0.000Neutral Zone n.a. 0.000 n.a. 0.000 0.000 0.000 0.000 0.000Niue n.a. 0.040 0.030 0.035 0.000 0.000 0.000 0.000Tokelau n.a. n.a. 0.023 0.023 0.000 0.000 0.000 0.000British Indian Ocean Ter. n.a. 0.003 0.000 0.001 0.000 0.000 0.000 0.000Fr. So. Ant. Tr n.a. n.a. 0.000 0.000 0.000 0.000 0.000 0.000Pitcairn n.a. 0.000 0.006 0.003 0.000 0.000 0.000 0.000Western Sahara n.a. n.a. 0.000 0.000 0.000 0.000 0.000 0.000Unweighted average 0.061 0.080 0.087 0.072 0.498 0.493 0.508 0.501


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7. CONCLUDING COMMENTS

This paper provides a comprehensive description of global IIT patterns. Anumber of broad results emerge:

• The share of IIT is on a secular upward trend, suggesting a gradualconvergence of the sector composition of national economies worldwide.

• The increase in IIT and the implied structural convergence are a high-incomeand middle-income phenomenon: while some, mainly Asian, lower-incomecountries exhibit rapidly increasing IIT shares, Africa has largely beenexcluded from this trend.

• Many indications point towards the importance of outward processing tradein explaining recent rises in IIT.

• The observed increase in IIT does not necessarily imply lower adjustmentcosts to trade expansion. MIIT is significantly lower than IIT, and no cleartime trend is discernible for MIIT.

The richness and detail of global trade data open the door to many conceivableextensions of this work. One potential avenue would be to explore not justbilateral IIT, but trilateral or more generally multilateral trade flows within thesame industry. This is of particular relevance for an analysis of the global dis-persion of product chains via outward processing trade. Another possibly fruitfulextension would be to explore the link between (M)IIT and factor reallocationin developing-country settings, all of the existing evidence on the ‘smooth-adjustment hypothesis’ being based on data for developed economies.

APPENDIX

Countries Included in the ‘Long Coverage’ Dataset (by World Bank Income Group)

Low-income and lower-middle income:Bolivia, Brazil, Colombia, Ecuador, Egypt, Guatemala, Honduras, Indonesia,India, Jordan, Morocco, Nicaragua, Pakistan, Peru, Philippines, Paraguay, ElSalvador, Thailand, Tunisia.

Upper-middle income:Argentina, Barbados, Chile, Costa Rica, Hungary, Mexico, Malaysia, Panama,Turkey, Venezuela.

High income:Australia, Austria, Belgium, Canada, Switzerland, Germany, Denmark, Spain,Finland, France, United Kingdom, Greece, Hong Kong, Ireland, Iceland, Israel,



Italy, Japan, Korea, Malta, Netherlands, Norway, New Zealand, Portugal, Singa-pore, Sweden, United States.

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Aquino, A. (1978), ‘Intra-Industry Trade and Inter-Industry Specialization as Concurrent Sourcesin International Trade in Manufactures’, Weltwirtschaftliches Archiv, 114, 2, 275–96.

Azhar, A. K. M. and R. J. R. Elliott (2004), ‘On the Measurement of Trade Induced Adjustment’,Review of World Economics, 139, 3, 419–39.

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